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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".