Aspiration Thrombectomy: 2-Dimensional Operative Video
Bibliographic record
Abstract
Acute ischemic stroke is one of the leading causes of disability and mortality worldwide. There have been significant improvements to the treatment of acute ischemic stroke over the past 5 yr, specifically related to strokes caused by large vessel occlusions. Stent retrievers with and without local aspiration and direct aspiration alone have all been demonstrated as viable treatment options for this patient population. This case represents the surgical technique for direct aspiration for the treatment of large vessel occlusion. A 76-yr-old man presented with right-sided weakness and aphasia. His last known normal was 5 h ago. His NIHSS (National Institutes of Health Stroke Scale) was 18. The noncontrast computed tomography (CT) did not show a significant infarct burden and ASPECTS (Alberta Stroke Program Early CT Score) of 9. CT angiogram demonstrated a left M1 occlusion. The patient was not a candidate for tissue plasminogen activator (tPA) because of time to presentation; however, the patient was deemed to be a candidate for emergent thrombectomy. Consent was obtained per institutional guidelines for the emergent procedure and the video recording. The video demonstrates a direct aspiration thrombectomy technique for the treatment of stroke. The patient successfully underwent direct aspiration thrombectomy with a TICI 3 (thrombolysis in cerebral infarction) recanalization.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".