The challenges of measuring bleeding outcomes in clinical trials of platelet transfusions
Bibliographic record
Abstract
BACKGROUND: Many platelet (PLT) transfusion trials now use bleeding as a primary outcome; however, previous studies have shown a wide variation in the amount (5%-70%) and type of bleeding documented. Differences in the way bleeding has been identified, recorded, and graded may account for some of this variability. This study's aim was to compare trials' method to document and grade bleeding. STUDY DESIGN AND METHODS: Data were collected via three methods: a review of study publications, study case report forms, and a questionnaire sent to the authors. Authors of randomized controlled trials of PLT transfusion that used bleeding as an outcome measure were identified from the searches reported by two recent systematic reviews. Twenty-four authors were contacted, and 13 agreed to participate. Data submitted were reviewed and summarized. RESULTS: More recent studies with trained bleeding assessors, detailed documentation, and expanded grading systems have reported higher overall levels of bleeding. The World Health Organization grading system was widely used to grade bleeding, but there was no consistency in the bleeding grade definitions. For example, bleeding classified as Grade 2 in some studies (spreading petechiae) was classified as Grade 1 in other studies. CONCLUSIONS: This study has highlighted differences in the method of recording and grading bleeding, which may account for some of the variation in reported bleeding rates. To ensure that differences between studies can be attributed to trial interventions or types of participant included, this study group is developing consensus bleeding definitions, a standardized approach to record and grade bleeding, and guidance notes to educate and train bleeding assessors.
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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 | 0.861 | 0.932 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.023 | 0.027 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.013 | 0.013 |
| Research integrity | 0.008 | 0.012 |
| 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; 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".