CLEAR Thrombectomy Score: An Index to Estimate the Probability of Good Functional Outcome With or Without Endovascular Treatment in the Late Window for Anterior Circulation Occlusion
Bibliographic record
Abstract
Background With the expanding eligibility for endovascular therapy (EVT) of patients presenting in the late window (6–24 hours after last known well), we aimed to derive a score to predict favorable outcomes associated with EVT versus best medical management. Methods and Results A multinational observational cohort of patients from the CLEAR (Computed Tomography for Late Endovascular Reperfusion) study with proximal intracranial occlusion (2014–2022) was queried (n=58 sites). Logistic regression analyses were used to derive a 9‐point score for predicting good functional outcome (modified Rankin Scale score 0–2 or return to premorbid modified Rankin Scale score) at 90 days, with sensitivity analyses for prespecified subgroups conducted using bootstrapped random forest regressions. Secondary outcomes included 90‐day functional independence (modified Rankin Scale score 0–2), poor outcome (modified Rankin Scale score 5–6), and 90‐day survival. The score was externally validated with a single‐center cohort (2014–2023). Of the 3231 included patients (n=2499 EVT), a 9‐point score included age, early computed tomography ischemic changes, and stroke severity, with higher points indicating a higher probability of a good functional outcome. The areas under the curve for the primary outcome among EVT and best medical management subgroups were 0.72 (95% CI, 0.70–0.74) and 0.87 (95% CI, 0.84–0.90), respectively, with similar performance in the external validation cohort (area under the curve, 0.71 [95% CI, 0.66–0.76]). There was a significant interaction between the score and EVT for good functional outcome, functional independence, and poor outcome (all P interaction <0.001), with greater benefit favoring patients with lower and midrange scores. Conclusions This score is a pragmatic tool that can estimate the probability of a good outcome with EVT in the late window. Registration URL: https://www.Clinicaltrials.gov ; Unique identifier: NCT04096248.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".