Reference notes

Coverage

57 journals · 17 benchmark fields

Sources

Journal Intelligence Dataset + publisher sources

Last reviewed

February 2026

Prepared by the Manusights editorial team.

Biomedical Journal Acceptance Rates: Free Reference Guide (2026)

Q: What is a typical acceptance rate for top biomedical journals?

Acceptance rates vary widely by journal tier. Flagship journals like Nature, Science, Cell, NEJM, and Lancet accept 5-10% or fewer of submitted manuscripts. High-impact specialty journals (Nature Medicine, JAMA, Circulation) typically accept 8-15%. Mid-tier specialty journals (Journal of Clinical Oncology, Neuron, Cell Metabolism) accept 15-25%. Broad-scope open access journals like PLOS ONE and Scientific Reports accept 40-55%. These figures represent overall acceptance rates - desk rejection rates at top journals can be 60-70%, meaning fewer than one-third of submissions even reach peer review.

Q: How does desk rejection rate differ from acceptance rate?

Desk rejection is when an editor rejects a manuscript before sending it to external reviewers, usually within days of submission. This happens when the scope doesn't fit the journal, the work lacks novelty for that journal's readership, or the manuscript has obvious methodological flaws. At Nature and Cell, desk rejection rates are 60-75%. At NEJM and Lancet, they reach 80-90%. The post-peer-review acceptance rate (papers accepted among those that survive desk review) is therefore much higher than the overall acceptance rate. Understanding both rates helps calibrate submission strategy.

Q: Should I target journals where I have the highest chance of acceptance?

Not necessarily. Targeting only high-acceptance journals sacrifices visibility and career impact. The optimal strategy is to submit to the highest-tier journal where your work is genuinely competitive - not the one with the best odds, but the one where your paper fits the scope and novelty bar. A paper rejected from Nature and published in Nature Communications still carries significant weight. Papers published only in low-IF journals because authors were risk-averse often go unread. A structured pre-submission review of your manuscript can help accurately assess where it is competitive before you commit to a submission target.

Biomedical journal acceptance rates are difficult to compare because publishers report them inconsistently and subscription databases rarely expose the underlying context. This page brings those benchmarks together for 57 journals in one searchable reference table.

The dataset combines published journal statistics, editorial transparency reports, and Manusights editorial normalization, with methodology notes preserved where denominators and evidence quality differ.

Updated Feb 2026

Biomedical journal acceptance rates by field

Search and export acceptance-rate ranges, impact factors, and methodology notes for 57 journals across clinical, translational, and basic-science fields. Indicative ranges remain visible where publisher reporting methods are not directly comparable.

57 journals17 benchmark fieldsCanonical dataset rows

Canonical source

This table now projects fully from the Journal Intelligence Dataset, so acceptance-rate rows, field labels, and notes all come from the same maintained source of truth.

⚠️ Important: How Acceptance Rates Are Calculated Varies

Not all journals calculate acceptance rates the same way. Some include desk rejections in the denominator (giving a lower rate). Others report only manuscripts that made it to peer review. A few report per-track rates. This makes direct comparisons between journals imprecise.

Where we know a journal's methodology, we note it in the table. The figures here should be treated as indicative ranges, not exact thresholds. A journal reporting "8%" using all submissions may actually be more competitive than one reporting "15%" counting only reviewed manuscripts.

Biomedical journal acceptance rates by journal

Filter by field, journal name, or methodology note. Export the current view for journal selection, benchmarking, or lab planning.

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Clinical Medicine

Blood

Field

Clinical Medicine

IF (2024)

23.1

Acceptance rate

~20%

Notes

Canonical dataset row (estimated); hematology relevance is broad and not limited to one narrow disease niche

Clinical Medicine

BMC Medicine

Field

Clinical Medicine

IF (2024)

8.3

Acceptance rate

~20%

Notes

Canonical dataset row (estimated); the paper is clinically relevant and broad enough for a serious general-medicine OA venue

Clinical Medicine

BMJ Open

Field

Clinical Medicine

IF (2024)

2.3

Acceptance rate

~27%

Notes

Canonical dataset row (estimated); the paper is sound, well reported, and appropriate for a broad clinical OA venue

Neuroscience

Brain

Field

Neuroscience

IF (2024)

11.7

Acceptance rate

~15%

Notes

Canonical dataset row (estimated); the paper has real clinical-neurology or translational consequence rather than only a narrow mechanistic result

Oncology

Cancer Cell

Field

Oncology

IF (2024)

44.5

Acceptance rate

~8–10%

Notes

Canonical dataset row (estimated); oncology consequence and mechanistic depth are both clear

Cell & Molecular Biology

Cell

Field

Cell & Molecular Biology

IF (2024)

42.5

Acceptance rate

<8%

Notes

Canonical dataset row (estimated); the mechanism is both complete and broadly interesting

Microbiology

Cell Host & Microbe

Field

Microbiology

IF (2024)

18.7

Acceptance rate

~12%

Notes

Canonical dataset row (estimated); the host-pathogen mechanism is sharp enough to matter beyond one organism-specific niche

Cell & Molecular Biology

Cell Metabolism

Field

Cell & Molecular Biology

IF (2024)

30.9

Acceptance rate

~5–8%

Notes

Canonical dataset row (estimated); metabolic consequence and mechanistic depth are both strong

Cell & Molecular Biology

Cell Reports

Field

Cell & Molecular Biology

IF (2024)

6.9

Acceptance rate

~15–20%

Notes

Canonical dataset row (estimated); the study is complete and biologically useful even if not Cell-level broad

Stem Cell Biology

Cell Stem Cell

Field

Stem Cell Biology

IF (2024)

20.4

Acceptance rate

~10%

Notes

Canonical dataset row (estimated); the paper changes how stem-cell or regenerative-biology people think, not just one technical corner

Cardiology

Circulation

Field

Cardiology

IF (2024)

38.6

Acceptance rate

~7%

Notes

Canonical dataset row (estimated); cardiovascular consequence is broad and timely

Cardiology

Circulation Research

Field

Cardiology

IF (2024)

16.2

Acceptance rate

~10%

Notes

Canonical dataset row (estimated); the paper has clear cardiovascular-mechanism relevance and not just one narrow model-system result

Cell & Molecular Biology

Current Biology

Field

Cell & Molecular Biology

IF (2024)

7.5

Acceptance rate

~35%

Notes

Canonical dataset row (estimated); the result is interesting and well-packaged even if it does not need a flagship Cell Press venue

Developmental Biology

Developmental Cell

Field

Developmental Biology

IF (2024)

8.7

Acceptance rate

~18%

Notes

Canonical dataset row (estimated); the developmental mechanism is clean, complete, and interesting beyond one phenotype description

Life Sciences

eLife

Field

Life Sciences

IF (2024)

N/A

Acceptance rate

~15%

Notes

Canonical dataset row (estimated); the paper is strong enough to benefit from public review and transparent editorial assessment

Cardiology

European Heart Journal

Field

Cardiology

IF (2024)

35.6

Acceptance rate

~10%

Notes

Canonical dataset row (estimated); cardiology-wide relevance is obvious from the first screen

Immunology

Frontiers in Immunology

Field

Immunology

IF (2024)

5.9

Acceptance rate

~40%

Notes

Canonical dataset row (estimated); the study is technically solid and clearly within section scope

Clinical Medicine

Gastroenterology

Field

Clinical Medicine

IF (2024)

25.1

Acceptance rate

~12%

Notes

Canonical dataset row (estimated); the paper matters broadly across GI rather than one narrow disease lane

Genomics & Methods

Genome Biology

Field

Genomics & Methods

IF (2024)

9.4

Acceptance rate

~15%

Notes

Canonical dataset row (estimated); the genomics contribution is broadly useful and not just dataset-sized

Clinical Medicine

GUT

Field

Clinical Medicine

IF (2024)

25.8

Acceptance rate

~12%

Notes

Canonical dataset row (estimated); the paper has broad GI or hepatology relevance and strong execution

Clinical Medicine

Hepatology

Field

Clinical Medicine

IF (2024)

15.8

Acceptance rate

~15%

Notes

Canonical dataset row (estimated); the paper matters to hepatology broadly rather than a single liver niche

Immunology

Immunity

Field

Immunology

IF (2024)

26.3

Acceptance rate

~8–10%

Notes

Canonical dataset row (estimated); the immune mechanism is broad, clear, and conceptually important

Cardiology

JACC

Field

Cardiology

IF (2024)

22.3

Acceptance rate

~5%

Notes

Canonical dataset row (estimated); the study matters to practicing cardiologists at a broad level

Clinical Medicine

JAMA

Field

Clinical Medicine

IF (2024)

55.0

Acceptance rate

<5%

Notes

Canonical dataset row (reported); clinical message is broad-interest and immediately legible

Cardiology

JAMA Cardiology

Field

Cardiology

IF (2024)

14.1

Acceptance rate

~8%

Notes

Canonical dataset row (estimated); the manuscript matters to clinical cardiology, not just one research niche

Oncology

JAMA Oncology

Field

Oncology

IF (2024)

20.1

Acceptance rate

~8%

Notes

Canonical dataset row (estimated); the manuscript has clear oncology-practice relevance

Clinical Medicine

Journal of Clinical Investigation

Field

Clinical Medicine

IF (2024)

13.6

Acceptance rate

~8–10%

Notes

Canonical dataset row (estimated); mechanistic disease relevance is strong enough to matter clinically

Oncology

Journal of Clinical Oncology

Field

Oncology

IF (2024)

41.9

Acceptance rate

~15%

Notes

Canonical dataset row (estimated); clinical relevance is strong enough to matter to oncologists broadly

Neuroscience

Journal of Neuroscience

Field

Neuroscience

IF (2024)

4.0

Acceptance rate

~25%

Notes

Canonical dataset row (estimated); the study is methodologically rigorous even if it does not need a flagship neuroscience venue

Infectious Diseases

Lancet Infectious Diseases

Field

Infectious Diseases

IF (2024)

31.0

Acceptance rate

~12%

Notes

Canonical dataset row (estimated); the study has obvious infectious-disease or global-health consequence beyond a narrow pathogen story

Neuroscience

Lancet Neurology

Field

Neuroscience

IF (2024)

45.5

Acceptance rate

~10%

Notes

Canonical dataset row (estimated); the paper matters to neurology practice or field-wide neurologic thinking, not only one neuroscience niche

Cell & Molecular Biology

Molecular Cell

Field

Cell & Molecular Biology

IF (2024)

16.6

Acceptance rate

~13%

Notes

Canonical dataset row (estimated); mechanistic novelty is strong enough to matter beyond one narrow niche

Psychiatry

Molecular Psychiatry

Field

Psychiatry

IF (2024)

10.1

Acceptance rate

~12%

Notes

Canonical dataset row (estimated); the paper connects neuroscience or genetics to psychiatry in a way that feels clinically or conceptually durable

Multidisciplinary

Nature

Field

Multidisciplinary

IF (2024)

48.5

Acceptance rate

<8%

Notes

Canonical dataset row (estimated); cross-disciplinary importance is obvious without explanation theater

Genomics & Methods

Nature Biotechnology

Field

Genomics & Methods

IF (2024)

41.7

Acceptance rate

<10%

Notes

Canonical dataset row (estimated); the platform matters beyond one use case

Chemical Biology

Nature Chemical Biology

Field

Chemical Biology

IF (2024)

13.7

Acceptance rate

~15%

Notes

Canonical dataset row (estimated); the chemistry changes a biological question rather than decorating it

Multidisciplinary

Nature Communications

Field

Multidisciplinary

IF (2024)

15.7

Acceptance rate

~20%

Notes

Canonical dataset row (estimated); the study is novel and complete even if not Nature-level broad

Genomics & Methods

Nature Genetics

Field

Genomics & Methods

IF (2024)

29.0

Acceptance rate

<10%

Notes

Canonical dataset row (estimated); the genetics advance is broadly important beyond one cohort or locus

Immunology

Nature Immunology

Field

Immunology

IF (2024)

27.6

Acceptance rate

~5–8%

Notes

Canonical dataset row (estimated); the immune mechanism is both sharp and broadly field-shaping

Clinical Medicine

Nature Medicine

Field

Clinical Medicine

IF (2024)

50.0

Acceptance rate

<8%

Notes

Canonical dataset row (estimated); translational or medical consequence is integral to the main story

Genomics & Methods

Nature Methods

Field

Genomics & Methods

IF (2024)

32.1

Acceptance rate

~8–10%

Notes

Canonical dataset row (estimated); the method changes what many labs can do, not just one narrow workflow

Neuroscience

Nature Neuroscience

Field

Neuroscience

IF (2024)

20.0

Acceptance rate

~9%

Notes

Canonical dataset row (estimated); the neuroscience claim feels both field-shaping and robust to skeptical specialist review

Structural Biology

Nature Structural & Molecular Biology

Field

Structural Biology

IF (2024)

10.1

Acceptance rate

~12%

Notes

Canonical dataset row (estimated); the structural insight changes biological understanding rather than adding a narrower structure report

Neuroscience

Neuron

Field

Neuroscience

IF (2024)

15.0

Acceptance rate

~8%

Notes

Canonical dataset row (estimated); the neuroscience consequence is broad and conceptually strong

Clinical Medicine

New England Journal of Medicine

Field

Clinical Medicine

IF (2024)

78.5

Acceptance rate

<5%

Notes

Canonical dataset row (reported); the manuscript changes clinical practice rather than adding incremental evidence

Genomics & Methods

Nucleic Acids Research

Field

Genomics & Methods

IF (2024)

13.1

Acceptance rate

~45%

Notes

Canonical dataset row (estimated); the paper fits a strong NAR lane such as methods, databases, or nucleic-acid biology

Clinical Medicine

PLOS Medicine

Field

Clinical Medicine

IF (2024)

9.9

Acceptance rate

~15%

Notes

Canonical dataset row (estimated); the paper has real clinical or public-health consequence and fits an open-access general-medicine audience

Multidisciplinary

PLOS ONE

Field

Multidisciplinary

IF (2024)

2.6

Acceptance rate

~31%

Notes

Canonical dataset row (reported); methods and reporting are clean enough for a soundness-based venue

Multidisciplinary

PNAS

Field

Multidisciplinary

IF (2024)

9.1

Acceptance rate

~15%

Notes

Canonical dataset row (estimated); the paper has broad enough scientific interest for a general-science venue without needing Nature/Science-level reach

Multidisciplinary

Science

Field

Multidisciplinary

IF (2024)

45.8

Acceptance rate

<7%

Notes

Canonical dataset row (estimated); the result changes how a broad scientific audience thinks

Multidisciplinary

Science Advances

Field

Multidisciplinary

IF (2024)

12.5

Acceptance rate

~10%

Notes

Canonical dataset row (estimated); the paper has broad scientific interest without needing Science-level field-shifting force

Translational Medicine

Science Translational Medicine

Field

Translational Medicine

IF (2024)

14.6

Acceptance rate

~15%

Notes

Canonical dataset row (estimated); the paper truly bridges bench and bedside instead of only borrowing translational language

Multidisciplinary

Scientific Reports

Field

Multidisciplinary

IF (2024)

3.9

Acceptance rate

~57%

Notes

Canonical dataset row (estimated); the study is technically sound and publishable even without prestige-tier novelty

Clinical Medicine

The BMJ

Field

Clinical Medicine

IF (2024)

42.7

Acceptance rate

~7% overall; ~4% research

Notes

Canonical dataset row (reported); the paper has broad general-medical or public-health value

Cell & Molecular Biology

The EMBO Journal

Field

Cell & Molecular Biology

IF (2024)

8.3

Acceptance rate

~15%

Notes

Canonical dataset row (estimated); the manuscript has a mature molecular or cell-biology story with real mechanistic closure

Clinical Medicine

The Lancet

Field

Clinical Medicine

IF (2024)

88.5

Acceptance rate

<5%

Notes

Canonical dataset row (estimated); the paper matters across medicine or public health, not only a subspecialty

Oncology

The Lancet Oncology

Field

Oncology

IF (2024)

35.9

Acceptance rate

~8%

Notes

Canonical dataset row (estimated); oncology consequence is broad enough to matter outside one tumor niche

Field	Journal	IF (2024)	Acceptance rate	Notes
Clinical Medicine	Blood	23.1	~20%	Canonical dataset row (estimated); hematology relevance is broad and not limited to one narrow disease niche
Clinical Medicine	BMC Medicine	8.3	~20%	Canonical dataset row (estimated); the paper is clinically relevant and broad enough for a serious general-medicine OA venue
Clinical Medicine	BMJ Open	2.3	~27%	Canonical dataset row (estimated); the paper is sound, well reported, and appropriate for a broad clinical OA venue
Neuroscience	Brain	11.7	~15%	Canonical dataset row (estimated); the paper has real clinical-neurology or translational consequence rather than only a narrow mechanistic result
Oncology	Cancer Cell	44.5	~8–10%	Canonical dataset row (estimated); oncology consequence and mechanistic depth are both clear
Cell & Molecular Biology	Cell	42.5	<8%	Canonical dataset row (estimated); the mechanism is both complete and broadly interesting
Microbiology	Cell Host & Microbe	18.7	~12%	Canonical dataset row (estimated); the host-pathogen mechanism is sharp enough to matter beyond one organism-specific niche
Cell & Molecular Biology	Cell Metabolism	30.9	~5–8%	Canonical dataset row (estimated); metabolic consequence and mechanistic depth are both strong
Cell & Molecular Biology	Cell Reports	6.9	~15–20%	Canonical dataset row (estimated); the study is complete and biologically useful even if not Cell-level broad
Stem Cell Biology	Cell Stem Cell	20.4	~10%	Canonical dataset row (estimated); the paper changes how stem-cell or regenerative-biology people think, not just one technical corner
Cardiology	Circulation	38.6	~7%	Canonical dataset row (estimated); cardiovascular consequence is broad and timely
Cardiology	Circulation Research	16.2	~10%	Canonical dataset row (estimated); the paper has clear cardiovascular-mechanism relevance and not just one narrow model-system result
Cell & Molecular Biology	Current Biology	7.5	~35%	Canonical dataset row (estimated); the result is interesting and well-packaged even if it does not need a flagship Cell Press venue
Developmental Biology	Developmental Cell	8.7	~18%	Canonical dataset row (estimated); the developmental mechanism is clean, complete, and interesting beyond one phenotype description
Life Sciences	eLife	N/A	~15%	Canonical dataset row (estimated); the paper is strong enough to benefit from public review and transparent editorial assessment
Cardiology	European Heart Journal	35.6	~10%	Canonical dataset row (estimated); cardiology-wide relevance is obvious from the first screen
Immunology	Frontiers in Immunology	5.9	~40%	Canonical dataset row (estimated); the study is technically solid and clearly within section scope
Clinical Medicine	Gastroenterology	25.1	~12%	Canonical dataset row (estimated); the paper matters broadly across GI rather than one narrow disease lane
Genomics & Methods	Genome Biology	9.4	~15%	Canonical dataset row (estimated); the genomics contribution is broadly useful and not just dataset-sized
Clinical Medicine	GUT	25.8	~12%	Canonical dataset row (estimated); the paper has broad GI or hepatology relevance and strong execution
Clinical Medicine	Hepatology	15.8	~15%	Canonical dataset row (estimated); the paper matters to hepatology broadly rather than a single liver niche
Immunology	Immunity	26.3	~8–10%	Canonical dataset row (estimated); the immune mechanism is broad, clear, and conceptually important
Cardiology	JACC	22.3	~5%	Canonical dataset row (estimated); the study matters to practicing cardiologists at a broad level
Clinical Medicine	JAMA	55.0	<5%	Canonical dataset row (reported); clinical message is broad-interest and immediately legible
Cardiology	JAMA Cardiology	14.1	~8%	Canonical dataset row (estimated); the manuscript matters to clinical cardiology, not just one research niche
Oncology	JAMA Oncology	20.1	~8%	Canonical dataset row (estimated); the manuscript has clear oncology-practice relevance
Clinical Medicine	Journal of Clinical Investigation	13.6	~8–10%	Canonical dataset row (estimated); mechanistic disease relevance is strong enough to matter clinically
Oncology	Journal of Clinical Oncology	41.9	~15%	Canonical dataset row (estimated); clinical relevance is strong enough to matter to oncologists broadly
Neuroscience	Journal of Neuroscience	4.0	~25%	Canonical dataset row (estimated); the study is methodologically rigorous even if it does not need a flagship neuroscience venue
Infectious Diseases	Lancet Infectious Diseases	31.0	~12%	Canonical dataset row (estimated); the study has obvious infectious-disease or global-health consequence beyond a narrow pathogen story
Neuroscience	Lancet Neurology	45.5	~10%	Canonical dataset row (estimated); the paper matters to neurology practice or field-wide neurologic thinking, not only one neuroscience niche
Cell & Molecular Biology	Molecular Cell	16.6	~13%	Canonical dataset row (estimated); mechanistic novelty is strong enough to matter beyond one narrow niche
Psychiatry	Molecular Psychiatry	10.1	~12%	Canonical dataset row (estimated); the paper connects neuroscience or genetics to psychiatry in a way that feels clinically or conceptually durable
Multidisciplinary	Nature	48.5	<8%	Canonical dataset row (estimated); cross-disciplinary importance is obvious without explanation theater
Genomics & Methods	Nature Biotechnology	41.7	<10%	Canonical dataset row (estimated); the platform matters beyond one use case
Chemical Biology	Nature Chemical Biology	13.7	~15%	Canonical dataset row (estimated); the chemistry changes a biological question rather than decorating it
Multidisciplinary	Nature Communications	15.7	~20%	Canonical dataset row (estimated); the study is novel and complete even if not Nature-level broad
Genomics & Methods	Nature Genetics	29.0	<10%	Canonical dataset row (estimated); the genetics advance is broadly important beyond one cohort or locus
Immunology	Nature Immunology	27.6	~5–8%	Canonical dataset row (estimated); the immune mechanism is both sharp and broadly field-shaping
Clinical Medicine	Nature Medicine	50.0	<8%	Canonical dataset row (estimated); translational or medical consequence is integral to the main story
Genomics & Methods	Nature Methods	32.1	~8–10%	Canonical dataset row (estimated); the method changes what many labs can do, not just one narrow workflow
Neuroscience	Nature Neuroscience	20.0	~9%	Canonical dataset row (estimated); the neuroscience claim feels both field-shaping and robust to skeptical specialist review
Structural Biology	Nature Structural & Molecular Biology	10.1	~12%	Canonical dataset row (estimated); the structural insight changes biological understanding rather than adding a narrower structure report
Neuroscience	Neuron	15.0	~8%	Canonical dataset row (estimated); the neuroscience consequence is broad and conceptually strong
Clinical Medicine	New England Journal of Medicine	78.5	<5%	Canonical dataset row (reported); the manuscript changes clinical practice rather than adding incremental evidence
Genomics & Methods	Nucleic Acids Research	13.1	~45%	Canonical dataset row (estimated); the paper fits a strong NAR lane such as methods, databases, or nucleic-acid biology
Clinical Medicine	PLOS Medicine	9.9	~15%	Canonical dataset row (estimated); the paper has real clinical or public-health consequence and fits an open-access general-medicine audience
Multidisciplinary	PLOS ONE	2.6	~31%	Canonical dataset row (reported); methods and reporting are clean enough for a soundness-based venue
Multidisciplinary	PNAS	9.1	~15%	Canonical dataset row (estimated); the paper has broad enough scientific interest for a general-science venue without needing Nature/Science-level reach
Multidisciplinary	Science	45.8	<7%	Canonical dataset row (estimated); the result changes how a broad scientific audience thinks
Multidisciplinary	Science Advances	12.5	~10%	Canonical dataset row (estimated); the paper has broad scientific interest without needing Science-level field-shifting force
Translational Medicine	Science Translational Medicine	14.6	~15%	Canonical dataset row (estimated); the paper truly bridges bench and bedside instead of only borrowing translational language
Multidisciplinary	Scientific Reports	3.9	~57%	Canonical dataset row (estimated); the study is technically sound and publishable even without prestige-tier novelty
Clinical Medicine	The BMJ	42.7	~7% overall; ~4% research	Canonical dataset row (reported); the paper has broad general-medical or public-health value
Cell & Molecular Biology	The EMBO Journal	8.3	~15%	Canonical dataset row (estimated); the manuscript has a mature molecular or cell-biology story with real mechanistic closure
Clinical Medicine	The Lancet	88.5	<5%	Canonical dataset row (estimated); the paper matters across medicine or public health, not only a subspecialty
Oncology	The Lancet Oncology	35.9	~8%	Canonical dataset row (estimated); oncology consequence is broad enough to matter outside one tumor niche

What Acceptance Rates Actually Tell You

They're not the same as your odds

A 5% acceptance rate doesn't mean you have a 1-in-20 shot. The pool of submitters at top journals includes many manuscripts that were never realistic candidates. If your work genuinely fits the scope and has the right methodological rigor, your effective acceptance rate within that subset is higher.

That said, a 5% rate tells you something real about the competitive bar. It means editors receive roughly 20 manuscripts for every one they publish. Your work doesn't just need to be good; it needs to be significantly better than 19 other good papers.

High rates don't mean easy

Nucleic Acids Research has a ~45% acceptance rate. That doesn't mean it's easy to publish there. It means the journal has a clear scope (nucleic acids and genomics tools) and if your work fits that scope and meets technical standards, it's likely to be accepted. Journals with high rates often have more precise scopes and fewer out-of-scope submissions in the first place.

PLOS ONE's 31% rate reflects a different model entirely: they accept any technically sound science, regardless of perceived impact. The "competition" is against your own methodology, not against other papers.

Use acceptance rates as one filter, not the only one

The most useful exercise is building a tiered shortlist: one reach journal (where you'd be thrilled to publish), one solid match (where your work genuinely fits), and one accessible option (where sound methodology is enough). Acceptance rates help you calibrate those tiers, but they work together with impact factor, scope fit, review timeline, and open access requirements.

For field-specific guidance on how to tier journals and what each tier requires, see the field guides.

Data Sources & Methodology

• Impact factors: Clarivate Journal Citation Reports, 2024 release
• Acceptance rates: Individual journal statistics pages, annual reports, and editorial transparency statements published by journals or their parent publishers (Cell Press, Springer Nature, NEJM Group, JAMA Network, BMJ Publishing Group, AHA Journals, ESC Publications, AAAS, PLOS, etc.)
• Where rates aren't officially published: Estimates synthesized from peer-reviewed analyses of submission patterns (e.g., Ware & Mabe, The STM Report, 4th ed.; Bjork et al., PLOS ONE, 2014) and editor interviews published in Nature, Science, and BMJ. All estimates are marked with "~" in the table.
• Primary sources (where verifiable): NEJM (nejm.org/author-center/new-manuscripts), Nature Medicine (nature.com/nm/author-instructions), PLOS (journals.plos.org/plosone/s/journal-information), BMJ (bmj.com/about-bmj), Clarivate JCR (jcr.clarivate.com)
• Last updated: February 2026. Impact factor data updated annually; acceptance rate data updated when journals publish new statistics.

Primary Sources

These are the authoritative sources for acceptance rate and publishing statistics data. Where journals publish their own figures, those take precedence over estimates.

Clarivate Journal Citation Reports (JCR) ↗

Authoritative source for impact factors. Annual release. Paywall, check via your library.

NEJM Annual Statistics ↗

NEJM publishes submission and acceptance statistics in its author instructions and annual editorial reports.

Nature Portfolio Author FAQ ↗

Nature family journals list acceptance rate context in author guidance pages.

PLOS Journal Metrics ↗

PLOS ONE journal information page includes acceptance rates and publication statistics.

BMJ Research Integrity (BMJ Statistics) ↗

BMJ Group publishes annual data on submissions, desk rejections, and acceptances.

AAAS (Science/Science Advances) Author Information ↗

AAAS provides submission statistics for Science and Science Advances.

Version History

February 2026

Re-reviewed acceptance-rate notes, aligned impact factor values to the current JCR baseline, and added exportable dataset utilities.

December 2025

Expanded the benchmark to 57 journals across 8 biomedical fields and added methodology notes for non-comparable publisher reporting methods.

Frequently Asked Questions

What is a typical acceptance rate for top biomedical journals?

Acceptance rates vary widely by journal tier. Flagship journals like Nature, Science, Cell, NEJM, and Lancet accept 5-10% or fewer of submitted manuscripts. High-impact specialty journals (Nature Medicine, JAMA, Circulation) typically accept 8-15%. Mid-tier specialty journals accept 15-25%. Broad-scope open access journals like PLOS ONE and Scientific Reports accept 40-55%. These figures represent overall acceptance rates - desk rejection rates at top journals can be 60-70%, meaning fewer than one-third of submissions even reach peer review.

How does desk rejection rate differ from acceptance rate?

Desk rejection is when an editor rejects a manuscript before sending it to external reviewers, usually within days of submission. This happens when the scope doesn't fit the journal, the work lacks novelty for that journal's readership, or the manuscript has obvious methodological flaws. At Nature and Cell, desk rejection rates are 60-75%. At NEJM and Lancet, they reach 80-90%. The post-peer-review acceptance rate (papers accepted among those that survive desk review) is therefore much higher than the overall acceptance rate.

Should I target journals where I have the highest chance of acceptance?

Not necessarily. Targeting only high-acceptance journals sacrifices visibility and career impact. The optimal strategy is to submit to the highest-tier journal where your work is genuinely competitive - not the one with the best odds, but the one where your paper fits the scope and novelty bar. A paper rejected from Nature and published in Nature Communications still carries significant weight. A structured pre-submission review of your manuscript can help accurately assess where it is competitive before you commit to a target journal.

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Peer review timelines

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