Reporting quality of pilot clinical trials in chronic kidney disease patients on hemodialysis: a methodological survey
Bibliographic record
Abstract
The conduct of high-quality pilot studies can help inform the success of larger clinical trials. Guidelines have been recently developed for the reporting of pilot trials. This methodological survey evaluates the completeness of reporting in pilot randomized controlled trials in chronic kidney disease patients on hemodialysis (HD patients) and explores factors associated with better completion of reporting. The authors searched Pubmed on July 1, 2018, for all pilot trials conducted in HD patients. Reporting quality was assessed against the 40-item Consolidated Standards of Reporting Trials (CONSORT) Extension for Pilot Trials. Study factors including year and country of publication, intervention, number of centers, type of funding, and journal endorsement of CONSORT were also examined. The mean number of items reported from the CONSORT extension for pilot trials across all included articles was 18.4 (standard deviation [SD] = 4.4). In the adjusted analysis, studies reported in later years (IRR = 1.026, 95% CI [1.018, 1.034], p < 0.001) and an increase of 20 persons in sample size (adjusted IRR = 1.021, 95% CI [1.010, 1.031], p < 0.001) were associated with a significantly higher number of CONSORT pilot items reported. Current reporting completeness of pilot trials in HD patients is suboptimal. Endorsing the CONSORT extension specific to pilot and feasibility studies and ensuring that pilot trials focus on the feasibility objectives may improve reporting completeness of these trials.
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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 | 0.750 | 0.886 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".