Comparison of conventional coagulation tests and <scp>ROTEM</scp> in identifying trauma‐induced coagulopathy for massive haemorrhage protocol activation
Bibliographic record
Abstract
Abstract Objectives Trauma‐induced coagulopathy (TIC) can be fatal but preventable if recognised early. With emerging uses of rotational thromboelastometry (ROTEM) to guide transfusions in trauma, patient outcomes with TIC‐defined by initial ROTEM and conventional coagulation tests (CCTs) during massive haemorrhage protocol (MHP) activations were evaluated at a primary trauma centre in British Columbia. Methods This retrospective observational study included adult trauma patients requiring MHP from June 1, 2020, to May 31, 2022. TIC, defined by initial results including (1) ROTEM‐based EXTEM A10 <40 mm, EXTEM CT >100 s, EXTEM ML30 >10%, FIBTEM A10 <10 mm; and (2) CCT‐based INR ≥1.8, PTT ≥1.5 times of upper normal limit, platelets <50 x 109/L, and Clauss Fibrinogen <1.5 g/L, was assessed for its correlation with mortality. Modified Poisson regression was used to model 28‐day mortality. Results Twenty‐two of sixty‐eight patients (32%) had abnormal ROTEM but normal CCTs. TIC defined by CCTs was associated with increased mortality [24 h: 5/13 (38%) vs. 5/55 (9%), p = 0.025; 28d: 8/13 (62%) vs. 11/55 (20%), p = 0.002]; compared to ROTEM, which was not [24 h: 7/35 (20%) vs. 3/33 (9%), p = 0.307; 28d: 11/35 (31%) vs. 9/33 (27%), p = 0.594], despite requiring significantly higher blood component transfusion within the first 4 and 24 h of MHP (p‐values<0.05). Conclusions ROTEM is more sensitive in identifying TIC. Patients with abnormal CCTs had a higher death rate, and those with abnormal ROTEM had no significantly increased mortality. A prospective study is required to assess the effects of ROTEM further.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".