MétaCan
Menu
Back to cohort
Record W4408936335 · doi:10.1016/s2214-109x(24)00514-x

Reporting summary results in clinical trial registries: updated guidance from WHO

2025· review· en· W4408936335 on OpenAlexaff
An‐Wen Chan, Ghassan Karam, Justin Pymento, Lisa Askie, Luiza Rosângela da Silva, Ségolène Aymé, Lotty Hooft, Anna Laura Ross, Vasee Moorthy

Bibliographic record

VenueThe Lancet Global Health · 2025
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of TorontoWomen's College Hospital
FundersWorld Health Organization
KeywordsMEDLINEMedicineClinical trialMedical physicsPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

The importance of publicly registering clinical trials and reporting their results in registries is widely recognised. While substantial progress has been made with registering trials before enrolment, the availability of results in registries remains uncommon despite expanding legislative and funder requirements-leading to an incomplete evidence base and avoidable waste of resources, particularly for unpublished trials. This paper discusses the rationale for reporting summary results in trial registries, reviews the current landscape of registry policies, and presents new WHO guidance for reporting results in registries. The 2025 WHO guidance was developed after consultation with relevant parties, including researchers, patients, sponsors, funders, regulators, journal editors, registry administrators, and the public. The guidance defines eight minimum items that are essential for understanding and interpreting the summary results for all trials. Implementation of the WHO guidance by trial registries, broad adherence by investigators and sponsors, and endorsement by funders, regulators, legislators, research ethics committees, patient organisations, and journals can help enhance the contribution of trials to scientific knowledge, patient care, and health policy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.127
metaresearch head score (Gemma)0.053
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1270.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0120.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.698
GPT teacher head0.597
Teacher spread0.101 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe Lancet Global HealthSame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207