Design, analysis, and reporting of pilot studies in HIV: a systematic review and methodological study
Bibliographic record
Abstract
BACKGROUND: Pilot studies are essential in determining if a larger study is feasible. This is especially true when targeting populations that experience stigma and may be difficult to include in research, such as people with HIV. We sought to describe how pilot studies have been used to inform HIV clinical trials. METHODS: We conducted a methodological study of pilot studies of interventions in people living with HIV published until November 25, 2020, using Medline, Embase, and Cochrane Controlled Register of Trials (CENTRAL). We extracted data on their nomenclature, primary objective, use of progression criteria, sample size, use of qualitative methods, and other contextual information (region, income, level, type of intervention, study design). RESULTS: Our search retrieved 10,597 studies, of which 248 were eligible. The number of pilot studies increased steadily over time. We found that 179 studies (72.2%) used the terms "pilot" or "feasibility" in their title, 65.3% tested feasibility as a primary objective, only 2% used progression criteria, 23.9% provided a sample size estimation and only 30.2% used qualitative methods. CONCLUSIONS: Pilot studies are increasingly being used to inform HIV research. However, the titles and objectives are not always consistent with piloting. The design and reporting of pilot studies in HIV could be improved.
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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 | 0.482 | 0.666 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.023 | 0.017 |
| Bibliometrics | 0.030 | 0.029 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".