Using rotational thromboelastometry clot firmness at 5 minutes (ROTEM <sup>®</sup> EXTEM A5) to predict massive transfusion and in‐hospital mortality in trauma: a retrospective analysis of 1146 patients
Bibliographic record
Abstract
are increasingly used to guide transfusion of blood products. The EXTEM assay maximum clot firmness (MCF) is a ROTEM measure available after 25-29 min used to guide early decisions. EXTEM A10, the clot firmness at 10 min, is an accepted early surrogate, but investigators differ on whether A5, the clot firmness at 5 min, is acceptable. We re-examined this in a retrospective observational analysis of 1146 trauma patients in one centre who had ROTEM data recorded. A5 and A10 both correlated well with maximum clot firmness, with Pearson coefficients of r = 0.92 and r = 0.96, respectively. The correlations of A5, A10 and maximum clot firmness with requirement for massive transfusion were all similarly high, with c-stats of 0.87, 0.89 and 0.90, respectively. The correlations with mortality were also similar but weaker, with c-stats of 0.67, 0.69 and 0.69, respectively. Using a previously validated cut-off of A5 < 35 mm to predict massive transfusion gave a sensitivity of 95%, specificity 83%, positive predictive value 9.3% and negative predictive value 100%. Using a value of A5 < 29 mm, for a pragmatic positive predictive value of 20%, gave a sensitivity of 67%, specificity 95% and negative predictive value 99%. Whether aiming for a high sensitivity or a strong predictive value, A5 was non-inferior to A10 and actually missed fewer cases needing massive transfusion. A5 has similar utility to both A10 and maximum clot firmness as an early measure of clot firmness, and a low A5 value is strongly predictive of the need for massive transfusion.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".