Simplified selection criteria for patients with longer or unknown time to treatment predict good outcome after mechanical thrombectomy
Bibliographic record
Abstract
OBJECTIVE: To identify simplified selection criteria for mechanical thrombectomy (MT) in longer and unknown time windows. METHODS: Patients with large vessel occlusion (LVO) in the anterior circulation who underwent MT between January 2014 and November 2017 were identified from the local registry. Patients were selected for analysis if they met the current guideline recommendation for MT treatment except for time window (HERMES-like) and were divided according to time they were last seen well (LSW): LSW <6 hours or LSW >6 hours before MT. The primary endpoint, good outcome, was modified Rankin scale score 0-2 on day 90. Safety outcomes were mortality on day 90 and symptomatic intracranial hemorrhage (sICH). Univariate and multivariate analysis were performed for good outcome in HERMES-like patients. RESULTS: In total, 752 patients were identified and 390 patients (51.9%) fulfilled the HERMES-like criteria. Despite differences in baseline parameters, more diffusion-weighted imaging (DWI) (43.9% vs 11.3%, p<0.001) and fewer cases of thrombolysis (32.7% vs 77%, p<0.001), patients LSW >6 hours (n=107) did not differ in the primary and secondary endpoints: good outcome (44.9% vs 44.9%, p=1.0), mortality (14% vs 15.2%, p=0.87), and sICH (5.6% vs 6%, p=1.0). After multivariate regression analysis, independent predictors of good outcome remained: age, OR=0.96 (95% CI 0.95 to 0.98); National Institutes of Health Stroke Scale score, OR=0.92 (95% CI 0.89 to 0.96); Alberta Stroke Programme Early CT Score (ASPECTS), OR=1.26 (95% CI 1.06 to 1.49); general anesthesia, OR=0.2 (95% CI 0.04 to 0.99), and successful recanalization, OR=12 (95% CI 4.7 to 30.5); but not treatment time and DWI or CT perfusion at baseline. CONCLUSION: Patients with proven LVO in unknown and longer time windows may be selected for MT based on ASPECTS and clinical criteria.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".