Reporting guidelines (EQUATOR Network)

Last updated: 2026-05-17

Synthesis

Reporting guidelines are checklists that specify, study design by study design, what information a paper must report to be evaluable. They are not style advice; they are minimum-information standards, analogous to MIAME for microarrays or DICOM for imaging. The EQUATOR Network (equator-network.org), an international consortium founded in 2008, maintains the canonical registry — currently more than 600 guidelines across study designs and reporting domains.

For a scriptorium-style writing system, reporting guidelines are the validation contract. Where narrative frameworks (narrative-frameworks) tell you whether a paper is readable, reporting guidelines tell you whether it is evaluable. A trial paper that omits randomization details is not recoverable by good prose. Several scriptorium skills planned for v0.3 — in particular statistics-consistency, sample-size-validation, and a future reporting-guideline-compliance skill — are grounded directly in these checklists.

The major reporting guidelines have been published with explanation-and- elaboration companion documents that work as worked examples and are useful as the substrate for LLM-driven compliance checks. Adoption by journals is mixed: high-impact medical journals largely require CONSORT, PRISMA, and STROBE in their author guidelines; specialty and methods journals are patchier; AI-specific extensions are very new and not yet uniformly required.

Evidence and frameworks

CONSORT 2010 — randomized controlled trials

Schulz KF, Altman DG, Moher D (for the CONSORT Group). CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c332. DOI: 10.1136/bmj.c332. [1]

CONSORT is a 25-item checklist plus a participant flow diagram. The check covers trial design, randomization details, blinding, sample size justification, primary and secondary outcomes, statistical methods, harms, and limitations. It is required (or “strongly encouraged”) by most major clinical journals and by ICMJE-member journals.

The extensions are now numerous: cluster trials, non-inferiority/equivalence trials, pragmatic trials, herbal interventions, and — most relevant for modern manuscripts — CONSORT-AI (see below).

STROBE — observational studies

von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP (for the STROBE Initiative). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. PLoS Med. 2007;4(10):e296. DOI: 10.1371/journal.pmed.0040296. [2]

A 22-item checklist for cohort, case-control, and cross-sectional studies (18 items common; 4 design-specific). STROBE is the de facto standard for epidemiological reporting and the foundation for several extensions (STROBE-ME for molecular epidemiology, RECORD for routinely collected health data, STROBE-Vet for veterinary studies).

PRISMA 2020 — systematic reviews

Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. DOI: 10.1136/bmj.n71. [3]

PRISMA 2020 replaced the 2009 statement (Moher et al., DOI: 10.1136/bmj.b2535). The 27-item checklist (with an expanded abstract checklist and revised flow diagrams) addresses search strategy, study selection, risk-of-bias assessment, certainty assessment, and synthesis methods. PRISMA extensions exist for network meta-analyses, scoping reviews, diagnostic test accuracy reviews, and individual participant data.

ARRIVE 2.0 — animal research

Percie du Sert N, Hurst V, Ahluwalia A, et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 2020; 18(7):e3000410. DOI: 10.1371/journal.pbio.3000410. [4]

ARRIVE 2.0 reorganized the 2010 original into two priority tiers: the “Essential 10” (the absolute minimum, including study design, sample size, inclusion/exclusion criteria, randomization, blinding, outcome measures, statistical methods, experimental animals, experimental procedures, and results) and a Recommended Set covering ethics, housing, contextual detail. Adoption by funders (Wellcome, NIH NC3Rs) has driven uptake.

STARD 2015 — diagnostic accuracy

Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015;351:h5527. DOI: 10.1136/bmj.h5527. [5]

A 30-item checklist for studies estimating the accuracy of a diagnostic test against a reference standard. Co-published in BMJ, Radiology, and Clinical Chemistry in October 2015. A STARD-AI extension was published in 2025 (Sounderajah V, et al. Nat Med. 2025; DOI: 10.1038/s41591-025-03953-8) to address artificial-intelligence-based diagnostic tools.

TRIPOD and TRIPOD+AI — prediction models

Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. BMJ. 2015;350:g7594. DOI: 10.1136/bmj.g7594. [6]

TRIPOD covers the development, validation, or update of multivariable prediction models. The 22-item checklist addresses outcomes, predictors, sample size, missing data, model specification, performance, and external validation.

Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:q824. DOI: 10.1136/bmj.q824. [7] (The full statement: BMJ 2024;385:e078378.)

TRIPOD+AI expanded the checklist to 27 items and unified reporting for regression-based and ML-based models. It places new emphasis on trustworthiness, fairness, and reproducibility — including reporting of training/validation/test splits, hyperparameter selection, and fairness across demographic subgroups.

CONSORT-AI and SPIRIT-AI — AI-enabled trials

Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020;26:1364–1374. DOI: 10.1038/s41591-020-1034-x. [8]

Cruz Rivera S, Liu X, Chan A-W, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020;26:1351–1363. DOI: 10.1038/s41591-020-1037-7. [9]

These were the first international standards for trial reporting and protocols of AI-based interventions. SPIRIT-AI covers protocols (before the trial); CONSORT-AI covers final trial reports. Both add items for AI intervention description, input data handling, error analysis, and human/AI interaction.

Beyond these: the EQUATOR Network

The EQUATOR Network maintains the registry at equator-network.org. Other relevant guidelines include:

• CARE — case reports (DOI: 10.7453/gahmj.2013.008)
• SRQR / COREQ — qualitative research synthesis and reporting
• AGREE II — practice guidelines
• REMARK — tumor marker studies
• CHEERS 2022 — health economic evaluations (DOI: 10.1136/bmj-2021-067975)

For any given manuscript, the EQUATOR search tool returns the applicable guidelines by study design. A scriptorium skill that auto-detects design type from manuscript content could route to the right checklist automatically; this is a candidate v0.3 capability.

How this informs scriptorium

Reporting guidelines are the cleanest validation substrate available because they are enumerable. Each guideline ships as a numbered checklist, and each checklist item has a canonical answer pattern. This is ideal ground for structured-output skills.

Direct connections:

• statistics-consistency (planned, v0.3). Should ground its checks in the statistics items of the relevant guideline (CONSORT items 7a, 12; STROBE item 12; TRIPOD items 8, 9). Inconsistencies between methods and results in those specific items are higher-severity than free-text arithmetic mismatches.
• sample-size-validation (planned). Should detect whether sample-size justification is present (CONSORT 7a; ARRIVE Essential 10 item 2; STROBE item 10) and whether the justification names the assumed effect size, power, and alpha. The skill should not invent these — only flag absence.
• reporting-guideline-compliance (proposed new skill). A direct mapping from manuscript sections to checklist items. Output: per-item status (present / partial / missing / not-applicable) with a span pointer for each “present” claim. This is a critique skill, not transformative.
• journal-style-conversion (planned). Reporting guidelines are part of journal style. A conversion skill should know which guideline each target journal requires and check that the manuscript complies before reformatting citations or section titles.
• reviewer-simulation (current). The simulator should consult the applicable guideline as a baseline of reviewer concerns — many real reviewer comments are paraphrases of checklist items the manuscript didn’t satisfy.

A practical design note: the manuscript-state schema should be able to record which reporting guideline(s) apply, so skills don’t have to re-detect. Adding an optional reporting_guidelines: [CONSORT-AI, TRIPOD+AI] field to MANUSCRIPT_STATE.yaml would let any skill consume the answer.

Open questions / weak evidence

• Adoption rates vary widely. Studies of CONSORT compliance show 50–70% reporting completeness in journals that require it (e.g., Turner et al. Trials 2012); compliance for newer guidelines is lower.
• Whether guideline compliance improves outcomes (reduced research waste, more accurate meta-analyses) is plausible but not directly established by RCT-grade evidence — the natural experiment is hard to run.
• The AI extensions are very new. CONSORT-AI / SPIRIT-AI / TRIPOD+AI have been adopted by Nature Medicine, BMJ, and Lancet Digital Health but field-wide uptake is still patchy.
• EQUATOR catalogs hundreds of guidelines; many overlap, many are dormant. Auto-detecting which guideline applies to a given manuscript is non-trivial. A naïve routing rule (e.g., design-type keyword match) will misroute prospectively-collected cohort studies that look like trials.

References

1. Schulz KF, Altman DG, Moher D; CONSORT Group. CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c332. DOI: 10.1136/bmj.c332.
2. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. PLoS Med. 2007;4(10):e296. DOI: 10.1371/journal.pmed.0040296.
3. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. DOI: 10.1136/bmj.n71.
4. Percie du Sert N, Hurst V, Ahluwalia A, et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 2020;18(7):e3000410. DOI: 10.1371/journal.pbio.3000410.
5. Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015;351:h5527. DOI: 10.1136/bmj.h5527.
6. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. BMJ. 2015;350:g7594. DOI: 10.1136/bmj.g7594.
7. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:q824. (Full statement: BMJ 2024;385:e078378.) [TODO verify exact DOI of full statement]
8. Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020;26:1364–1374. DOI: 10.1038/s41591-020-1034-x.
9. Cruz Rivera S, Liu X, Chan A-W, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020;26:1351–1363. DOI: 10.1038/s41591-020-1037-7.
10. EQUATOR Network. Enhancing the QUAlity and Transparency Of health Research. https://www.equator-network.org/ (accessed 2026-05-17).