Mechanical Thrombectomy for M2 Occlusions: A Single-Centre Experience
Bibliographic record
Abstract
BACKGROUND: The recent success of several mechanical thrombectomy trials has resulted in a significant change in the management of patients presenting with stroke. However, questions still remain as to whether certain groups will benefit from mechanical thrombectomy. In particular, it is still uncertain whether mechanical thrombectomy should be performed in the M2 branches and, more generally, in the distal vasculature. METHODS: We retrospectively analysed our prospectively maintained database of all patients undergoing mechanical thrombectomy between January 2008 and August 2016. We collected demographic, radiological, procedural and outcome data. RESULTS: We identified 106 patients that met our inclusion criteria. The mean age of the patients was 68 ± 13.8 years, and there were 58 (54.7%) male patients. Associated medical conditions were common with hypertension seen in 71% of the patients. The average Alberta Stroke Program Early CT (ASPECT) score on admission was 8.5 ± 1.7. The mean National Institutes of Health Stroke Scale score was 11.8 ± 7.02. The mean duration of the procedure was 103 ± 3.4 min, and the average number of thrombectomy attempts required was 1.8 (range 1-8). Angiographically, Thrombolysis in Cerebral Infarction Scale (TICI) ≥2b was obtained in 90.5% of the patients. Five patients (4.7%) had symptomatic intracranial haemorrhage on follow-up. At 90-day follow-up, 54.6% of the patients had a modified Rankin Scale (mRS) score 0-2, and 71.5% had an mRS score ≤3. There were 15 deaths at 90 days (14.1%). CONCLUSION: Mechanical thrombectomy in patients with solitary M2 clots is technically possible and carries a high degree of success with a good safety profile. Patients with confirmed M2 occlusion should be considered for mechanical thrombectomy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".