Comparison of massive blood transfusion predictive models in the rural setting
Bibliographic record
Abstract
BACKGROUND: Hemorrhage is the leading cause of preventable death in trauma patients, of which 3% require massive transfusion (MT). MT predictive models such as the Assessment of Blood Consumption (ABC), Trauma-Associated Severe Hemorrhage (TASH), and McLaughlin scores have been developed, but only included patients requiring blood transfusion during their hospital stay, excluding a large percentage of trauma patients. Our purpose was to validate these MT predictive models in our rural Level I trauma center patient population, using all major trauma victims, regardless of blood product requirements. METHODS: Review of all Level I trauma patients admitted in 2008 to 2009 was performed. ABC, TASH, and McLaughlin scores were calculated using 80% probability for the need for MT. RESULTS: Three hundred seventy-three patients were admitted; 13% had a penetrating mechanism and 52% were scene transports. MT patients had higher Injury Severity Score (median, 43 vs. 13; p < 0.001) and lower Trauma-Injury Severity Score (0.310 vs. 0.983; p < 0.001). Mortality was higher in MT patients (18.4% vs. 5.4%; p < 0.009). Thirty-eight (10%) required MT; 34 were predicted by ABC, one by TASH, and six by McLaughlin. ABC (area under the receiver operating characteristic [AUROC] = 0.86) was predictive of MT, whereas TASH (AUROC = 0.51) and McLaughlin (AUROC = 0.56) were not. CONCLUSIONS: The ABC score correctly identified 89% of MT patients and was predictive of MT in major trauma patients at our rural Level I trauma center; the TASH and McLaughlin scores were not. The ABC score is simpler, faster, and more accurate. Based on this work, we strongly recommend adoption of the ABC score for MT prediction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".