Prediction of 90 day home time among patients with low baseline ASPECTS undergoing endovascular thrombectomy: results from Alberta’s Provincial Stroke Registry (QuICR)
Bibliographic record
Abstract
BACKGROUND: The benefit of endovascular thrombectomy (EVT) in stroke patients with a low baseline Alberta Stroke Program Early CT Score (ASPECTS, ≤5) is uncertain. We aim to use random forest regression modeling to predict 90 day home time in patients with low ASPECTS. METHODS: We used the Quality Improvement and Clinical Research (QuICR) provincial stroke registry and administrative data from southern Alberta to identify patients who underwent EVT in our center from July 2015 to November 2020. Baseline ASPECTS on non-contrast CT and CT angiography data were scored by a two physician consensus. The primary outcome was the predicted 90 day home time (the number of nights a patient is back at their premorbid living situation without an increase in level of care within 90 days of the stroke) using random forests regression. Estimates were generated using 200 bootstrapped datasets. Covariate contribution to home time was determined using partial dependence plots. RESULTS: Of 657 EVT patients, 85 (12.9%) had baseline ASPECTS ≤5 (mean age 70.9 years, 44.7% women, 93.9% good-moderate collaterals, 60% M1-middle cerebral artery occlusion). Using partial dependence estimates, mean predicted home times were similar in the low ASPECTS (44.3 days) versus higher ASPECTS (43.1) groups. Factors predicting lower 90 day home time in this population were diabetes mellitus (-8.8 days), hypertension (-5.7 days), and atrial fibrillation (-3.6 days). There was no meaningful difference in predicted 90 day home time by sex, baseline National Institutes of Health Stroke Severity Scale score, occlusion site, tandem lesion, collateral grade or thrombolysis. CONCLUSIONS: Patients with low ASPECTS who are selected for EVT using demographic and clinical profiles similar to higher ASPECTS patients achieved comparable outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".