Adjudicating Bleeding Outcomes in a Large Thromboprophylaxis Trial in Critical Illness.
Bibliographic record
Abstract
Abstract Abstract 2471 Poster Board II-448 Background: Adjudication in clinical trials can confirm or refute eligibility, describe cointerventions, judge appropriateness of care, or assess the severity of morbidity outcomes. Objective: To refine the adjudication process, calibrate 4 adjudicators, and measure agreement on bleeding severity in an international trial of heparin thromboprophylaxis (PROTECT). Methods: Independently and blinded to each others' ratings and study drug, 4 adjudicators used web-based methods to examine 40 charts of patients assessed by local research coordinators to have either major (20 patients) or minor (20 patients) bleeding. We discussed reasons for disagreement after the first 20 charts to remediate and recalibrate. We calculated crude agreement, chance-corrected agreement (kappa), and chance-independent agreement (phi). Results: For 45 events in 40 patients, pair-wise crude agreement ranged from 86.7-93.3% (average 82.2%). Overall kappa was 0.81. Phi (which can only analyze pair-wise values) ranged from 0.75-0.87. We resolved all disagreements. During adjudication discussions, we 1) addressed methodological issues (e.g., handling recurrent bleeds), 2) added a category (e.g., no bleed), 3) expanded a category (e.g., a major bleed did not have to be overt if other criteria were fulfilled), and 4) divorced procedure-grounded definitions (such as the need for transfusion or therapeutic interventions) from bleeding severity criteria (e.g., the patient could still be classified as having no bleed or a minor bleed if 2 units of PRBCs were transfused for anemia). Conclusions: After independent quadruplicate review of 45 bleeding events, we documented satisfactory agreement for bleeding outcomes, refined the adjudication process, and calibrated adjudicators for the remainder of the trial. Henceforth, charts will be randomly allocated to pairs of adjudicators for blinded review. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.298 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".