Associations between clinicoradiological variables and onset-to-imaging time in acute ischemic stroke: A retrospective study
Bibliographic record
Abstract
BACKGROUND: Many acute ischemic stroke (AIS) patients have an unknown onset time. These patients require advanced imaging or are sometimes excluded from treatment. We investigated the associations between clinicoradiological variables, as assessed with non-contrast computed tomography (NCCT) and CT angiography (CTA), and onset-to-imaging time in AIS patients included in the MR CLEAN registry, an endovascular treatment (EVT) registry. METHODS: We included 4003 AIS patients from the MR CLEAN Registry. Patients were classified as in the early (≤4.5 h; ≤6 h) or late (>4.5 h; >6 h) onset-to-imaging time windows. Univariable and multivariable logistic regression assessed associations between baseline clinicoradiological variables and onset-to-imaging time. Accuracy was evaluated using receiver operating characteristic (ROC) curve analyses. RESULTS: In multivariable logistic regression, Alberta Stroke Program Early CT Score (ASPECTS) (OR 0.90, 95 % CI 0.84-0.97), presence of leukoaraiosis (OR 1.51, 95 % CI 1.19-1.92), and moderate (OR 2.53, 95 % CI 1.16-5.54) and good (OR 3.90, 95 % CI 1.76-8.66) collateral score were significantly associated with an >4.5 h onset-to-imaging time. ASPECTS (OR 0.89, 95 % CI 0.81-0.98), presence of leukoaraiosis (OR 1.55, 95 % CI 1.14-2.11), and a good (OR 3.31, 95 % CI 1.17-9.39) collateral score were significantly associated with an >6 h onset-to-imaging time. Area under the ROC curves was 0.63 for both the >4.5 h and >6 h time windows. CONCLUSION: Among AIS patients included in the MR CLEAN registry, lower ASPECTS, presence of leukoaraiosis and a higher collateral score are associated with a late onset-to-imaging time (>4.5 h; >6 h after stroke onset). The accuracy of the association between clinicoradiological variables and onset-to-imaging time is poor.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".