Reporting of surrogate endpoints in randomised controlled trial protocols (SPIRIT-Surrogate): extension checklist with explanation and elaboration
Bibliographic record
Abstract
Randomised controlled trials often use surrogate endpoints to substitute for a target outcome (an outcome of direct interest and relevance to trial participants, clinicians, and other stakeholders—eg, all cause mortality) to improve efficiency (through shortened duration of follow-up, reduced sample size, and lower research costs), and for ethical or practical reasons. However, their use has a fundamental limitation in terms of uncertainty of the intervention effect on the target outcome and limited information on potential intervention harms. There have been increasing calls for improved reporting of trial protocols that use surrogate endpoints. This report presents the SPIRIT-Surrogate, an extension of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklist, a consensus driven reporting guideline designed for trial protocols using surrogate endpoints as the primary outcome(s). The SPIRIT-Surrogate extension includes nine items modified from the SPIRIT 2013 checklist. The guideline provides examples and explanations for each item. We recommend that all stakeholders (including trial investigators and sponsors, research ethics reviewers, funders, journal editors, and peer reviewers) use this extension in reporting trial protocols that use surrogate endpoints. Its use will allow for improved design of such trials, improved transparency, and interpretation of findings when trials are completed, and ultimately reduced 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 | 0.403 | 0.629 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".