More pilot trials could plan to use qualitative data: a meta-epidemiological study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Pilot trials often use quantitative data such as recruitment rate and retention rate to inform the design and feasibility of a larger trial. However, qualitative data such as patient, healthcare provider, and research staff perceptions of an intervention may also provide insights for a larger trial. METHODS: As part of a larger study investigating the reporting of progression criteria in pilot studies, we sought to determine how often pilot studies planned to use qualitative data to inform the design and feasibility of a larger trial and the factors associated with plans to use qualitative data. We searched for protocols of pilot studies of randomized trials in PubMed between 2013 and 2017. RESULTS: We included 227 articles. Only 92 (40.5%; 95% confidence interval [CI] 34.1-47.2) reported plans to collect qualitative data. The factors associated with collecting qualitative data were large studies (defined as sample size ≥ 60; adjusted odds ratio [aOR] 2.77; 95% CI 1.47-5.23; p = 0.002) and studies from Europe (aOR 3.86; 95% CI 1.68-8.88; p = 0.001) compared to North America and the rest of the world. Pilot trials with pharmacological interventions were less likely to plan to collect qualitative data (aOR 0.20; 95% CI 0.07-0.58; p = 0.003). CONCLUSIONS: Qualitative data is not used enough in pilot trials. Large pilot trials, pilot trials from Europe, and pilot trials of non-pharmacological interventions are more likely to plan for qualitative data.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Meta-epidemiology (narrow)Meta-epidemiology (broad)Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.354 | 0.684 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it