A comparison of clinical prediction scores for massive traumatic hemorrhage
Bibliographic record
Abstract
BACKGROUND: Accurate early identification of bleeding trauma patients remains challenging. Several clinical prediction tools-including the Assessment of Blood Consumption (ABC) score, Trauma-Associated Severe Hemorrhage (TASH) score, and shock index (SI)-have been developed to guide transfusion decisions, but their performance across clinically meaningful outcomes remains uncertain. METHODS: We conducted a retrospective cohort study of trauma patients with massive hemorrhage protocol (MHP) activation at a university-affiliated, regional referral trauma center in Ontario, Canada, from July 2019 to September 2022. We included patients aged ≥ 16 years who presented within 3 h of injury. We evaluated the ABC score, TASH score, and SI for predicting massive transfusion (≥ 10 PRBCs in 24 h or ≥ 5 PRBCs in 4 h), the critical administration threshold (CAT; ≥3 PRBCs in 1 h), need for hemostatic intervention, and hemorrhage-related mortality. Score performance was assessed using area under the ROC curve (AUC), sensitivity, and specificity. RESULTS: Among 331 patients, 10.6% received ≥ 10 PRBCs, 20.8% met the 5-unit threshold, 30.8% met CAT, 27.8% required hemostatic intervention, and 4.2% died from hemorrhage during the index admission. The TASH score had the highest AUCs (0.72-0.82) but poor sensitivity. The ABC score showed moderate, threshold-dependent performance (AUCs 0.66-0.76). The shock index (≥ 1.0) showed fair discrimination for major transfusion thresholds (AUC ~ 0.74) but was less predictive for hemostatic intervention (AUC 0.60). CONCLUSION: The ABC, TASH, and SI scores performed poorly to moderately across key bleeding outcomes. These findings highlight the need for improved tools aligned with real-time, clinically actionable endpoints in trauma resuscitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".