The revised Canadian Bleeding (CAN-BLEED) score for risk stratification of bleeding trauma patients: a mixed retrospective—prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Traumatic hemorrhage is a significant cause of morbidity and mortality. There is considerable interest in risk stratification tools to aid with early activation of intervention pathways for bleeding patients. In this study, we refine the Canadian Bleeding (CAN-BLEED) score for the prediction of major interventions in bleeding trauma patients. METHODS: We conducted a mixed retrospective-prospective cohort study. We included a retrospective cohort from the CAN-BLEED derivation study, from September 2014 to September 2017. We also conducted a prospective cohort from May 2019 to August 2021 and included both datasets for refinement of the CAN-BLEED score. The primary outcome was major intervention, defined by a composite of massive transfusion, embolization, or surgery for hemostasis. Predictors were pre-specified based on previous validation work. We used a stepdown procedure and regression coefficients to create a clinical risk stratification score. We used bootstrap internal validation to assess optimism-corrected performance. RESULTS: We included 1368 patients in the overall cohort. Incidence of penetrating injury was 23% and median injury severity score was 17. The overall incidence of the need for major intervention was 17%. The revised score included 8 variables: systolic blood pressure, heart rate, lactate, penetrating mechanism, pelvic instability, Focused Abdominal Sonography for Trauma positive for free fluid, computed tomography positive for free fluid, or contrast extravasation. The C-statistic for the simplified score is 0.89. A score cut-off of less than 2 points yielded a 97% (94-98%) sensitivity in ruling out the need for major intervention. CONCLUSION: The revised CAN-BLEED score offers a clinically intuitive and internally validated tool with excellent performance in identifying patients requiring major intervention for traumatic bleeding. Further efforts are required to evaluate its performance with an external validation.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".