Vitamin K‐dependent coagulation factor deficiency in trauma: a comparative analysis between international normalized ratio and thromboelastography (CME)
Bibliographic record
Abstract
BACKGROUND: The use of international normalized ratio (INR) to diagnose vitamin K-dependent coagulation factor (VitK-CF) deficiency in trauma has limitations (inability to predict bleeding and long turnaround times). Thromboelastography (TEG) assesses the entire coagulation process. With TEG, reaction time (TEG-R) is used to assess global coagulation factor activity and takes less than 10 minutes. We assessed the ability of TEG-R to detect VitK-CF deficiency in trauma, compared to the INR. STUDY DESIGN AND METHODS: A total of 219 trauma patients with INR, TEG, and all VitK-CF measured at admission were included. Demographics and laboratory tests, drugs, blood transfusions, and severity scores were analyzed. Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) of INR (≥1.3 and ≥1.5) and TEG-R (>8 min) to diagnose VitK-CF deficits (≤50%) were calculated. Secondary outcomes included time to INR and TEG results. RESULTS: Overall, TEG-R performed worse than INR. TEG-R had a sensitivity of 33% (95% CI, 16%-55%), specificity of 95% (95% CI, 91%-98%), PPV of 47% (95% CI, 23%-72%), and NPV of 92% (95% CI, 87%-95%). An INR of 1.5 or greater had a sensitivity of 67% (95% CI, 45%-84%), specificity of 98% (95% CI, 96%-99.7%), PPV of 84% (95% CI, 60%-97%), and NPV of 96% (95% CI, 92%-98%). An INR of 1.3 or greater also had better sensitivity, PPV, and NPV. For patients on warfarin, the times to INR results and TEG completion were 58 (±23) and 92 (±40) minutes (p=0.07), respectively. TEG-R was abnormal in only one patient on warfarin. CONCLUSION: Our study suggests that TEG-R is not superior at identifying VitK-CF deficiency compared to INR in trauma.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".