Repeated mechanical thrombectomy for acute ischemic stroke in a dialysis patient: A case report and literature review
Bibliographic record
Abstract
Mechanical thrombectomy (MT) can significantly improve the prognosis of patients with large vessel occlusion (LVO) stroke. It is still unclear whether repeated MT in a short period of time is safe and effective in patients with renal failure and atrial fibrillation (AF). We present the case of an LVO patient with AF and uremia who was successfully treated with MT for M1 segment occlusion of the right middle cerebral artery (MCA) and achieved a good outcome. Fifteen days after the first MT, the patient's stroke recurred; angiography at that time revealed M1 segment, and branch occlusion of the right MCA, and a second MT was performed. This patient was given oral warfarin to maintain her international normalized ratio (INR) between 2 and 3, and over a 9-month follow-up period, no further vascular events occurred. It may be safe and effective to perform repeated MTs in patients with uremia and AF who have suffered two cardiogenic strokes in a short period of time. It might be beneficial to treat a patient of this description with anticoagulant therapy after careful assessment of the patient's condition. Nephrologists and medical staff at hemodialysis centers should recognize the importance of MT for patients with acute ischemic stroke (AIS). In this way, health care providers can take measures in a timely, effective manner when they encounter hemodialysis patients with AIS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".