Reporting of surrogate endpoints in randomised controlled trial reports (CONSORT-Surrogate): extension checklist with explanation and elaboration
Bibliographic record
Abstract
Randomised controlled trials commonly use surrogate endpoints to substitute for a target outcome (outcome of direct interest and relevance to trial participants, clinicians, and other stakeholders—eg, all cause mortality) to improve their efficiency (through shorter trial duration, reduced sample size, and thus lower research costs), or for ethical or practical reasons. But reliance on surrogate endpoints can increase the uncertainty of an intervention’s treatment effect and potential failure to provide adequate information on intervention harms, which has led to calls for improved reporting of trials using surrogate endpoints. This report presents a consensus driven reporting guideline for trials using surrogate endpoints as the primary outcomes—the CONSORT (Consolidated Standards of Reporting Trials) extension checklist: CONSORT-Surrogate. The extension includes nine items modified from the CONSORT 2010 checklist and two new items. Examples and explanations for each item are provided. We recommend that all stakeholders (including trial investigators and sponsors, journal editors and peer reviewers, research ethics reviewers, and funders) use this extension in reporting trial reports using surrogate endpoints. Use of this checklist will improve transparency, interpretation, and usefulness of trial findings, and ultimately reduce research waste.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.397 | 0.694 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.039 | 0.018 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".