Adjudicating bleeding events in a platelet dose study: impact on outcome results and challenges
Bibliographic record
Abstract
BACKGROUND: In the SToP platelet dose study, the World Health Organization (WHO) bleeding grade was assigned using adjudication. This study describes the challenges associated with adjudicating bleeding events and compares the adjudicated and bedside results for bleeding grade. STUDY DESIGN AND METHODS: To categorize bleeding, the following information was provided to adjudicators: daily bleeding assessments, interventions to stop or control bleeding, daily blood counts, and transfused blood components. Each daily assessment was sent to two adjudicators who independently assigned a grade and anatomic site of bleeding. Discordant cases where disagreement occurred were sent to a third adjudicator and subsequently to a fourth or fifth adjudicator in an attempt to reach agreement. Disagreement after five adjudicators was resolved by consensus. The final adjudicated grade was compared with the grade of bleeding assigned at the bedside by study personnel. RESULTS: A total of 1150 case report forms were adjudicated. Disagreement on grade of bleeding was common: 31.2% after the first two adjudicators, 4.0% after the third adjudicator, 0.7% after four, and 0.05% after five. Disagreement on anatomic site was less but still occurred in 17% of cases after two adjudicators. The frequency of bleeding (≥ Grade 2) based on adjudication was higher than bedside grading (standard-dose arm, 47.5% vs. 34.4%; low-dose arm, 50.0% vs. 43.1%). CONCLUSION: The frequency of WHO bleeding varies depending on the method used to assign grade. Adjudication to assign bleeding grade resulted in significant disagreement when two adjudicators were used.
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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 | 0.296 | 0.374 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".