Predicting neuroimaging eligibility for extended-window endovascular thrombectomy
Bibliographic record
Abstract
OBJECTIVE: Endovascular thrombectomy (EVT) and tissue plasminogen activator (tPA) are effective ischemic stroke treatments in the initial treatment window. In the extended treatment window, these treatments may offer benefit, but CT and MR perfusion may be necessary to determine patient eligibility. Many hospitals do not have access to advanced imaging tools or EVT capability, and further patient care would require transfer to a facility with these capabilities. To assist transfer decisions, the authors developed risk indices that could identify patients eligible for extended-window EVT or tPA. METHODS: The authors retrospectively identified stroke patients who had concurrent CTA and perfusion and evaluated three potential outcomes that would suggest a benefit from patient transfer. The first outcome was large-vessel occlusion (LVO) and target mismatch (TM) in patients 5-23 hours from last known normal (LKN). The second outcome was TM in patients 5-15 hours from LKN with known LVO. The third outcome was TM in patients 4.5-12 hours from LKN. The authors created multivariable models using backward stepping with an α-error criterion of 0.05 and assessed them using C statistics. RESULTS: The final predictors included the National Institutes of Health Stroke Scale (NIHSS), the Alberta Stroke Program Early CT Score (ASPECTS), and age. The prediction of the first outcome had a C statistic of 0.71 (n = 145), the second outcome had a C statistic of 0.85 (n = 56), and the third outcome had a C statistic of 0.86 (n = 54). With 1 point given for each predictor at different cutoffs, a score of 3 points had probabilities of true positive of 80%, 90%, and 94% for the first, second, and third outcomes, respectively. CONCLUSIONS: Despite the limited sample size, compared with perfusion-based examinations, the clinical variables identified in this study accurately predicted which stroke patients would have salvageable penumbra (C statistic 71%-86%) in a range of clinical scenarios and treatment cutoffs. This prediction improved (C statistic 85%-86%) when utilized in patients with confirmed LVO or a less stringent tissue mismatch (TM < 1.2) cutoff. Larger patient registries should be used to validate and improve the predictive ability of these models.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".