Guidelines for reporting non-randomised pilot and feasibility studies
Bibliographic record
Abstract
increases, there is a need for good quality reporting guidelines to help researchers tailor their reports in a way that is consistent and helpful to other readers. The publication in 2016 of the CONSORT extension to pilot and feasibility trials filled a much-needed gap, but there still remains some uncertainty as to how to report pilot and feasibility studies that are not randomised. This editorial aims to provide some general guidance on how to report the most common types of non-randomised pilot and feasibility studies that are submitted to the journal. We recommend using the CONSORT extension to pilot and feasibility trials as the main reference document-it includes detailed elaboration and explanation of each item, and in most cases, simple adaptation, or non-use of items that are not applicable, will suffice. Several checklists found on the Equator website may provide helpful supplementary guidance, when used alongside the CONSORT extension, and we give some examples.
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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 | 0.539 | 0.854 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.036 | 0.045 |
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".