Pilot trials in thrombosis: Purpose and pitfalls
Bibliographic record
Abstract
Randomized controlled trials provide important evidence to guide clinical practice. These full-scale trials are expensive, time consuming and many are never successfully completed. Well conducted pilot studies help with full-scale trial design, assessment and optimization of feasibility, and can avoid the waste of resources associated with starting a full-scale trial that will not succeed. They also provide an opportunity for capacity growth and mentorship of new investigators. It is important to appreciate that the usual goal of a pilot trial is assessment of feasibility and refinement of trial design rather than to gain preliminary evidence of efficacy. Indeed, using event rates from a pilot trial to calculate sample sizes can be misleading in therapeutic trials. Misconceptions exist that pilot trials are just "small trials," are easy to perform, and are not worthy of publication. While, in the past, many pilot trials were poorly conducted and not followed by a full-scale trial, by following the recommendations in the "CONSORT 2010 statement: extension to randomized pilot and feasibility trials," high-quality pilot trials can be performed and reported that will greatly improve the chances of successfully completing a practice-changing trial. We propose that pilot trials are a valuable investment and describe the TRIM-Line pilot trial (NCT03506815), a pilot study assessing the feasibility of a randomized controlled trial investigating primary thromboprophylaxis with rivaroxaban in patients with malignancy and central venous catheters, as an illustrative example of how a pilot trial in the area of thrombosis should be designed.
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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 | 0.775 | 0.817 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.005 | 0.056 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.023 | 0.041 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".