A reporting guideline for clinical platelet transfusion studies from the BEST Collaborative
Bibliographic record
Abstract
BACKGROUND: A systematic review of randomized controlled trials and observational studies assessing platelet (PLT) transfusion therapy identified gaps in the descriptions of trial design, variables of the PLT products transfused, and outcomes. We aimed to systematically develop a reporting guideline to aid in designing, reporting, and critiquing PLT trials. STUDY DESIGN AND METHODS: With the use of expert opinion, a preliminary checklist of 23 items was created. The Delphi method, an iterative forecasting method, was used to achieve consensus among experts to systematically improve upon the preliminary checklist. Items were ranked for inclusion using a 7-point Likert scale from "definitely should not" to "very important to" include. Criteria were established a priori based on the mean score: at least 5.5 accept, 2.6 to 5.4 intermediate, and not more than 2.5 eliminate. Intermediate items were edited and sent out in subsequent rounds for review. Three rounds were undertaken to determine the final checklist. RESULTS: Initially 33 experts participated, decreasing to 25 by the third round. The preliminary checklist consisted of 23 items spread over four sections: methods and intervention, PLT-specific outcomes, PLT-specific results, and PLT-specific adverse events. After three rounds of the Delphi method, the checklist was expanded and refined to include 30 items. The final checklist was further enhanced by adding an explanatory guide. CONCLUSION: Use of the Delphi method was successful in finding consensus on items to include in reports of a clinical PLT transfusion study. The final checklist and explanatory guide will be useful for authors and editors to improve the reporting of PLT transfusion 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 | 0.428 | 0.587 |
| Meta-epidemiology (narrow) | 0.007 | 0.009 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.036 | 0.035 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.025 | 0.015 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.015 | 0.019 |
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".