P.136 Evaluating outcome prediction models in endovascular treatment for acute ischemic stroke using baseline, treatment and post-treatment variables
Bibliographic record
Abstract
Background: Predicting outcomes after endovascular treatment (EVT) for acute ischemic stroke with baseline variables remains a challenge. We assessed the performance of stroke outcome prediction models for EVT in acute ischemic stroke in an iterative fashion using baseline, treatment-related and post-treatment variables. Methods: Data from the ESCAPE-NA1 trial were used to build 4 outcome prediction models using multi-variable logistic regression: Model 1 included baseline variables only that are available prior to treatment decision-making, model 2 included additional treatment-related variables, model 3 additional early post-treatment variables, and model 4 additional late post-treatment variables. The primary outcome was 90-day modified Rankin Scale score 0-2. Model performance was compared using the area under the curve (AUC). Results: Among 1,105 patients, good outcome was achieved by 666 (60.3%). When using baseline variables only (model 1), the AUC was 0.74 (95%CI:0.71-0.77); this iteratively improved when treatment and post-treatment variables were added to the models (model 2: AUC 0.77,95%CI: 0.74-0.80, model 3: AUC 0.80,95%CI:0.77-0.83, model 4: AUC 0.82, 95%CI:0.79-0.85). Conclusions: Predicting EVT outcomes using baseline variables alone is inaccurate in one in four patients, and may be inappropriate for patient selection. Even the most comprehensive models with treatment-related and post-treatment factors involve considerable uncertainty.
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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 | 0.033 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".