A longitudinal assessment of trial protocols approved by research ethics committees: The Adherance to SPIrit REcommendations in the UK (ASPIRE-UK) study
Bibliographic record
Abstract
BACKGROUND: To assess the quality of reporting of RCT protocols approved by UK research ethics committees before and after the publication of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guideline. METHODS: We had access to RCT study protocols that received ethical approval in the UK in 2012 (n=103) and 2016 (n=108). From those, we assessed the adherence to the 33 SPIRIT items (i.e. a total of 64 components of the 33 SPIRIT items). We descriptively analysed the adherence to SPIRIT guidelines as proportion of adequately reported items (median and interquartile range [IQR]) and stratified the results by year of approval and sponsor. RESULTS: The proportion of reported SPIRIT items increased from a median of 64.9% (IQR, 57.6-69.2%) in 2012 to a median of 72.5% (IQR, 65.3-78.3%) in 2016. Industry-sponsored RCTs reported more SPIRIT items in 2012 (median 67.4%; IQR, 64.1-69.4%) compared to non-industry-sponsored trials (median 59.8%; IQR, 46.5-67.7%). This gap between industry- and non-industry-sponsored trials increased in 2016 (industry-sponsored: median 75.6%; IQR, 71.2-79.0% vs non-industry-sponsored: median 65.3%; IQR, 51.6-76.3%). CONCLUSIONS: The adherence to SPIRIT guidelines has improved in the UK from 2012 to 2016 but remains on a modest level, especially for non-industry-sponsored RCTs.
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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 | 0.330 | 0.520 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".