Results of Therapy Using Oral Anticoagulants in the Acute Phase after Mechanical Thrombectomy
Bibliographic record
Abstract
Objective: The usage of oral anticoagulants (OACs) in the acute phase of cerebral infarction has increased, but the optimal timing for starting OACs after mechanical thrombectomy (MT) is unclear. We report the usage of OACs after MT at our hospital and evaluated the outcomes. Methods: OACs were selected as secondary preventive drugs for 64 patients who underwent MT for anterior circulatory embolism between July 2016 and January 2019. Of the 64 patients, 28 and 36 received direct oral anticoagulants (DOACs) and warfarin (Wf), respectively. We compared the frequency of intracranial hemorrhage in the acute phase and that of recurrent cerebral infarction within 30 days. Results: The median diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Scores + white matter (DWI-ASPECTS + W) score at admission was 7.5 (IQR 6-9)/8 (IQR, 6-9) in the DOACs group/Wf group. The rate of recanalization with modified thrombolysis in cerebral infarction (TICI) ≥2B by MT was 89.3/80.6%. In patients with subarachnoid hemorrhage (SAH) associated with MT and patients with hemorrhagic transformation (HT) on MRI the next day, administration was started after hemostasis. The median timing of the first anticoagulant administration was 3 (IQR, 2-4)/2 (IQR, 1-4) days. In the case of no HT the next day, the rate of new HT after 1 week was 7.1%/29.1%. In the case of HT the next day, the rate of HT deterioration the next day was 7.1%/16.6%. The percentage of symptomatic bleeding was 0%/2.8%. The percentage of recurrent cerebral infarction within 30 days was 0%/2.8%. Conclusion: OACs in the acute phase after MT can be safely used and are expected to be effective at preventing recurrence.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".