Defining feasibility and pilot studies in preparation for randomised controlled trials: using consensus methods and validation to develop a conceptual framework
Bibliographic record
Abstract
Pilot and feasibility studies underpin much of current health related research, including randomised controlled trials. The number of reports in which authors describe their studies as pilot or feasibility studies is increasing. In spite of recent attempts to define pilot and feasibility studies, the research community has differing views about these definitions. We will present a framework for defining pilot and feasibility studies conducted in advance of randomised controlled trials designed to assess the effect of an intervention. The framework was developed using: a Delphi consensus study (93 participants); an open meeting at the 2nd International Clinical Trials Methodology Conference in 2013 (15 participants); a review of definitions outside the health research context; an international expert consensus meeting (26 participants); and a review of published empirical studies described as pilot or feasibility (27 studies). The framework uses feasibility as an umbrella term with pilot studies as a subset of feasibility studies; it does not use mutually exclusive definitions of pilot and feasibility studies. The framework involves straightforward definitions and can be represented in a simple diagram. We will show how it is consistent with the common usage of these terms in the literature, and with the UK Medical Research Council framework for complex interventions. Additionally although the UK National Institute for Health Research does imply that feasibility and pilot studies are mutually exclusive, their definitions can be mapped onto the framework. This work is part of a larger programme of work on pilot and feasibility studies.
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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 | 0.856 | 0.796 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.011 | 0.026 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".