Reporting quality of trial protocols improved for non-regulated interventions but not regulated interventions: A repeated cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: To investigate the adherence of randomised controlled trial (RCT) protocols evaluating non-regulated interventions (including dietary interventions, surgical procedures, behavioural and lifestyle interventions, and exercise programmes) in comparison with regulated interventions to the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Statement. METHODS: We conducted a repeated cross-sectional investigation in a random sample of RCT protocols approved in 2012 (n = 257) or 2016 (n = 292) by research ethics committees in Switzerland, Germany, or Canada. We investigated the proportion of accurately reported SPIRIT checklist items in protocols of trials with non-regulated as compared to regulated interventions. RESULTS: Overall, 131 (24%) of trial protocols tested non-regulated interventions. In 2012, the median proportion of SPIRIT items reported in these protocols (59%, interquartile range [IQR], 53%-69%) was lower than in protocols with regulated interventions (median, 74%, IQR, 66%-80%). In 2016, the reporting quality of protocols with non-regulated interventions (median, 75%, IQR, 62%-83%) improved to the level of regulated intervention protocols, which had not changed on average. CONCLUSIONS: Reporting of RCT protocols evaluating non-regulated interventions improved between 2012 and 2016, although remained suboptimal. SPIRIT recommendations need to be further endorsed by researchers, ethics committees, funding agencies, and journals to optimize reporting of RCT protocols.
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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 | 0.457 | 0.655 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".