B.4 Time to intervention in anticoagulant-associated intracerebral hemorrhage: gaps in care and their effect on hematoma expansion
Bibliographic record
Abstract
Background: In intracerebral hemorrhage (ICH), hematoma expansion (HE) is a major predictor of mortality and morbidity. A rapid approach, including oral anticoagulant (OAC) reversal and blood pressure (BP) reduction, both <60 min from arrival, improves outcomes. We aimed to evaluate current time metrics in the management of anticoagulant-associated intracerebral hemorrhage (AAICH) and their impact on HE in a high-income setting. Methods: Consecutive AAICH patients presenting to a high-volume stroke center (2017-2023) were retrospectively identified. Clinical and imaging data were merged, with baseline and follow-up hematoma volumes quantified using 3D Slicer segmentation software. Results: Of 75 AAICH patients, 62 received antihypertensives and 52 OAC reversal, with median(IQR) times to BP control: 87.5 (61-207) minutes and median time to OAC reversal: 67.5 (49-96) minutes. Only 14 (23%) and 23 (44%) achieved treatment targets <60 minutes, respectively, and 7 (9%) patients achieving both targets. HE occurred in 27 of 48 patients with follow-up imaging. Median time to target BP was significantly longer in those with HE (186.5 (87-317) min) compared to those without HE (70 (56-104) min), p=0.01. Conclusions: Current management of AAICH remains heterogeneous, with considerable treatment delays regarding BP control and OAC reversal. These findings support the implementation of standardized protocols to optimize AAICH treatment.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".