Clinical Observation of Infarct Volume ≥150 mL in Endovascular Thrombectomy Treatment
Bibliographic record
Abstract
OBJECTIVES: Whether patients with infarct volume ≥150 mL could benefit from endovascular thrombectomy (EVT) remains unclear. METHODS: Patients (n=104) with anterior circulation Alberta Stroke Program Early Computed Tomography Score <6 were screened for infarct volume ≥150 mL using the Pullicino formula × (1-22%). The following were compared with the baseline at 90 days: the modified Rankin scale score (mRS) ≤3, mortality rate, symptomatic intracranial hemorrhage and any intracranial hemorrhage within 48 hours, and modified Thrombolysis in Cerebral Infarction (mTICI) ≥2b between the EVT and drug therapy (DT) groups. RESULTS: In patients with infarct volumes ≥150 mL, mRS≤3 at 90 days was higher in the EVT group than in the DT group [adjusted odds risk (aOR), 5.52; 95% CI: 1.10-28.24, P =0.04), and mTICI ≥2b at 82.8%. Intracranial hemorrhage within 48 hours occurred in 7 (24.1%) patients in the EVT group and 5 (14.7%) in the DT group (aOR, 0.75; 95% CI: 0.16-3.46; P =0.71). Older age (aOR, 0.94; 95% CI: 0.90-0.99, P =0.01), EVT treatment (aOR, 4.51; 95% CI: 1.60-12.78, P =0.01), and infarct volume ≥150 mL (aOR, 0.11; 95% CI: 0.04-0.31, P <0.01) were significantly associated with patient prognosis. CONCLUSIONS: Patients with infarct volume ≥150 mL who received EVT had a higher proportion of mRS≤3 compared with those who received DT. However, there was no statistically significant difference in intracranial hemorrhage and death between the groups. EVT, smaller infarct volume, and younger age were associated with a good prognosis. The findings require large sample data verification.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".