Guidelines for Reporting Outcomes in Trial Protocols
Bibliographic record
Abstract
Importance: Complete information in a trial protocol regarding study outcomes is crucial for obtaining regulatory approvals, ensuring standardized trial conduct, reducing research waste, and providing transparency of methods to facilitate trial replication, critical appraisal, accurate reporting and interpretation of trial results, and knowledge synthesis. However, recommendations on what outcome-specific information should be included are diverse and inconsistent. To improve reporting practices promoting transparent and reproducible outcome selection, assessment, and analysis, a need for specific and harmonized guidance as to what outcome-specific information should be addressed in clinical trial protocols exists. Objective: To develop harmonized, evidence- and consensus-based standards for describing outcomes in clinical trial protocols through integration with the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 statement. Evidence Review: Using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework, the SPIRIT-Outcomes 2022 extension of the SPIRIT 2013 statement was developed by (1) generation and evaluation of candidate outcome reporting items via consultation with experts and a scoping review of existing guidance for reporting trial outcomes (published within the 10 years prior to March 19, 2018) identified through expert solicitation, electronic database searches of MEDLINE and the Cochrane Methodology Register, gray literature searches, and reference list searches; (2) a 3-round international Delphi voting process (November 2018-February 2019) completed by 124 panelists from 22 countries to rate and identify additional items; and (3) an in-person consensus meeting (April 9-10, 2019) attended by 25 panelists to identify essential items for outcome-specific reporting to be addressed in clinical trial protocols. Findings: The scoping review and consultation with experts identified 108 recommendations relevant to outcome-specific reporting to be addressed in trial protocols, the majority (72%) of which were not included in the SPIRIT 2013 statement. All recommendations were consolidated into 56 items for Delphi voting; after the Delphi survey process, 19 items met criteria for further evaluation at the consensus meeting and possible inclusion in the SPIRIT-Outcomes 2022 extension. The discussions during and after the consensus meeting yielded 9 items that elaborate on the SPIRIT 2013 statement checklist items and are related to completely defining and justifying the choice of primary, secondary, and other outcomes (SPIRIT 2013 statement checklist item 12) prospectively in the trial protocol, defining and justifying the target difference between treatment groups for the primary outcome used in the sample size calculations (SPIRIT 2013 statement checklist item 14), describing the responsiveness of the study instruments used to assess the outcome and providing details on the outcome assessors (SPIRIT 2013 statement checklist item 18a), and describing any planned methods to account for multiplicity relating to the analyses or interpretation of the results (SPIRIT 2013 statement checklist item 20a). Conclusions and Relevance: This SPIRIT-Outcomes 2022 extension of the SPIRIT 2013 statement provides 9 outcome-specific items that should be addressed in all trial protocols and may help increase trial utility, replicability, and transparency and may minimize the risk of selective nonreporting of trial results.
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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 | 0.541 | 0.756 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.024 | 0.032 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.026 | 0.014 |
| Open science | 0.014 | 0.011 |
| Research integrity | 0.024 | 0.043 |
| Insufficient payload (model declined to judge) | 0.031 | 0.047 |
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".