Thromboelastography may assess the effect of anticoagulation reversal in intracranial hemorrhage
Bibliographic record
Abstract
BACKGROUND: Intracranial hemorrhage (ICH) is a complication of oral anticoagulation and is associated with significant morbidity and mortality. Clinical need exists for biomarkers to measure anticoagulation in patients with factor Xa inhibitor-associated ICH to assess the hemostatic effect of reversal agents. This study explored the utility of thromboelastography (TEG) to assess anticoagulation in emergency department (ED) patients who received activated prothrombin complex concentrate (aPCC) reversal for factor Xa-inhibitor-associated ICH. METHODS: This was a prospective, single-center, cohort study in a convenient sample of adult patients presenting to the ED with acute factor Xa-associated ICH. Exclusion criteria included pregnancy, incarceration, polytrauma, hepatic failure, or other known coagulopathic conditions. TEG samples were collected prior to anticoagulation reversal, as well as at 30-minutes, 12-hours, and 24-hours post-reversal. Only patients who received aPCC reversal were included in the final analysis. RESULTS: Pre-reversal TEG was collected on 10 participants prior to aPCC administration. A significant decrease in TEG R-time was observed at 30 minutes post-aPCC reversal (Beta = -0.91, p = 0.035). R-time increased at 12- and 24-hours post-aPCC reversal to baseline levels. Significant changes were not observed in K-time, clot strength, maximum amplitude, or coagulation index. CONCLUSIONS: TEG R-time decreases acutely after anticoagulation reversal with aPCC and rebounds at 12- and 24-hours post-reversal. TEG R-time may serve as a potential sensitive biomarker of the residual anticoagulation activity of factor Xa inhibitors in patients with ICH that undergo anticoagulation reversal with aPCCs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".