Where have all the pilot studies gone? A follow-up on 30 years of pilot studies in <i>Clinical Rehabilitation</i>
Bibliographic record
Abstract
INTRODUCTION: Pilot studies are meritorious for determining the feasibility of a definitive clinical trial in terms of conduct and potential for efficacy, but their possible applications for planning a future trial are not always fully realized. The purpose of this review was to estimate the extent to which pilot/feasibility studies: (i) addressed needed objectives; (ii) led to definitive trials; and (iii) whether the subsequent undertaking of a definitive trial was influenced by the strength of the evidence of outcome improvement. METHODS: Trials published in the journal Clinical Rehabilitation, since its inception, were eligible if the word 'pilot' or 'feasibility' was specified somewhere in the article. A total of 191 studies were reviewed, results were summarized descriptively, and between-group effect sizes were computed. RESULTS: The specific purposes of piloting were stated in only 58% ( n = 110) of the studies. The most frequent purpose was to estimate the potential for efficacy (85%), followed by testing the feasibility of the intervention (60%). Only 12% of the studies were followed by a definitive trial; <4% of studies had a main study underway or a published study protocol. There was no relationship between observed effect size and follow-up of pilot studies, although the confidence intervals were very wide owing to small number of trials that followed on. DISCUSSION: Labelling and reporting of pilot studies needs to be improved to be concordant with the recently issued CONSORT guidelines. Feasibility needs to be fully tested and demonstrated prior to committing considerable human and monetary resources.
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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 | 0.408 | 0.547 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.028 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| 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".