Mechanical thrombectomy in stroke patients of advanced age with score-based prediction of outcome
Bibliographic record
Abstract
BACKGROUND: Stroke patients ≥80 years constituted only 15% in randomised trials on mechanical thrombectomy (MT), but is a considerable higher proportion in clinical practice. Association of clinical variables collected before MT with functional outcome has not been independently described in these patients, while being important in the decision of patient eligibility for MT. METHODS: We included patients consecutively at a single centre (2017-2021) categorised as octogenarians (age: 80-89 years) or nonagenarians (age: 90-99 years). Functional outcome at 90 days was defined as fair (modified Rankin Scale (mRS) 0-3) or poor (mRS 4-6). Clinical variables collected before MT were analysed for association with shift of mRS in a poor direction. Significant predictors were used to produce a risk score of fair outcome. Significance was set at the p < 0.05 level. RESULTS: Nonagenarians (n = 43, 15.5%) compared to octogenarians (n = 235, 84.5%) less likely achieved fair outcome (20.9% vs. 46.0%, p < 0.01) with higher mortality (65.1% vs. 31.9%, p < 0.01). Significant predictors of outcome were: age, adjusted odds ratio (aOR) = 0.91 (95% confidence interval (CI): 0.86-0.97); pre-stroke mRS, aOR = 0.57 (95% CI: 0.44-0.73); National Institute of Health Stroke Scale at admission, aOR = 0.91 (95% CI: 0.87-0.95); Alberta Stroke Program Early Computed Tomography, aOR = 1.23 (95% CI: 1.05-1.45). After bootstrap validation, the area under the curve of the risk score was 0.74 and the optimal cut-off for fair outcome was a score of >7 points. CONCLUSIONS: One in two octogenarians achieved fair outcome, however, only one in five nonagenarians had fair outcome. The clinical risk score could be considered as guidance when deciding patient eligibility for MT.
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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 | 0.004 |
| 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 | 0.001 |
| 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".