Adherence to local rotational thromboelastometry recommendations in the care of trauma patients: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Rotational thromboelastometry (ROTEM) is a blood test that measures hemostatic parameters to guide hemostatic therapy. ROTEM outputs can be cognitively challenging to interpret, which may limit adherence in trauma care. Our objective was to assess hemostatic therapy administration adherence to local ROTEM recommendations. STUDY DESIGN AND METHODS: We conducted a retrospective cohort study of trauma patients receiving ROTEM testing at a level 1 trauma center between January 1st 2017, and December 31st 2021. Adherence to local ROTEM best practices was determined by comparing the blood products patients received after a patient's first ROTEM test to those that should have been administered based on their ROTEM results. Multivariable logistic regression models were used to determine the association between clinical and patient covariates with ROTEM adherence and between ROTEM adherence and in-hospital mortality. RESULTS: Only 46.6% (n = 208/446) of patients had complete adherence to ROTEM recommendations. Product-specific adherence was lower when product initiation was recommended (vs. not) by ROTEM. A greater number of ROTEM abnormalities (odds ratio [OR]: 0.11, 95% confidence interval [CI]: 0.05-0.19) and a higher injury severity score (OR: 0.96, 95% CI: 0.94-0.98) reduced adherence. Adherence to ROTEM did not reduce in-hospital mortality (OR: 0.71, 95% CI: 0.35-1.41). The number of ROTEM abnormalities was associated with in-hospital mortality (OR: 3.07, 95% CI: 2.01-4.77). DISCUSSION: We found moderate adherence to admission ROTEM recommendations with lower adherence for more severely injured patients. The number of ROTEM abnormalities increased the odds of in-hospital mortality. Quantifying adherence is valuable for understanding ROTEM implementations in trauma care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".