Pilot and Feasibility Studies in Rehabilitation Research: A Review and Educational Primer for the Physiatrist Researcher.
Bibliographic record
Abstract
ABSTRACT: Pilot and feasibility studies are conducted early in the clinical research pathway to evaluate whether a future, definitive study can or should be done and, if so, how. Poor planning and reporting of pilot and feasibility studies can compromise subsequent research efforts. Inappropriate labeling of studies as pilots also compromises education. In this review, first, a systematic survey of the current state of pilot and feasibility studies in rehabilitation research was performed, and second, recommendations were made for improvements to their design and reporting. In a random sample of 100 studies, half (49.5%) were randomized trials. Thirty (30.0%) and three (3.0%) studies used "pilot" and "feasibility" in the study title, respectively. Only one third (34.0%) of studies provided a primary objective related to feasibility. Most studies (92.0%) stated an intent for hypothesis testing. Although many studies (70.0%) mentioned outcomes related to feasibility in the methods, a third (30.0%) reported additional outcomes in the results and discussion only or commented on feasibility anecdotally. The reporting of progression plans to a main study (21.0%) and progression criteria (4.0%) was infrequent. Based on these findings, it is recommended that researchers correctly label studies as a pilot or feasibility design based on accepted definitions, explicitly state feasibility objectives, outcomes, and criteria for determining success of feasibility, justify the sample size, and appropriately interpret and report the implications of feasibility findings for the main future study.
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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 | 0.158 | 0.290 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.028 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.010 | 0.015 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".