Abstract WP164: Prediction Of 90 Day Home Time Among Patients With Low Baseline Aspects Score Undergoing Endovascular Thrombectomy
Bibliographic record
Abstract
Background: The outcome in stroke patients with ASPECTS of ≤5 who undergo Endovascular Thrombectomy(EVT) in Large Vessel Occlusion (LVO) is uncertain. We used machine learning models to predict 90-day home-time in these patients. Methods: We used the QuICR provincial stroke registry and administrative data from Southern Alberta to identify patients who underwent EVT from Jan 2015-Dec 2019. Imaging data were scored by 2-physician consensus. The primary outcome was the predicted 90-day home-time(number of days a patient is back at their premorbid living situation without an increase in level of care within 90 days of the stroke) using generalized boosting machine model with Gaussian distribution. Covariate contribution to hometime was determined using partial dependence plots. Results: Of 659 EVT patients, 82(12%) had baseline ASPECTS ≤5(mean age 69.8y, 44.6% females, 93% good-moderate collaterals, M1 occlusion(64.1%). Overall, patients with low ASPECTS had lower median predicted home-time by 2.8d. Holding other covariates constant, factors predicting lower 90d-home-time were diabetes mellitus(-14d), hypertension(-7d), and symptomatic intracerebral hemorrhage (sICH) on follow up scan(-14d). Home-time decreased with increasing age in both low and non-low ASPECTS groups, but the difference was larger in older age groups (Figure). There was no meaningful difference in predicted 90d-home-time by sex, atrial fibrillation, baseline NIHSS, occlusion site, tandem lesion, thrombolysis, or successful reperfusion. Conclusions: Among patients with low ASPECTS who underwent EVT, hypertension, diabetes and sICH predicted lower 90-d home-time. .
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".