Stroke imaging prior to thrombectomy in the late window: results from a pooled multicentre analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Collateral assessment using CT angiography is a promising modality for selecting patients for endovascular thrombectomy (EVT) in the late window (6-24 hours). The outcome of these patients compared with those selected using perfusion imaging is not clear. METHODS: We pooled data from seven trials and registries of EVT-treated patients in the late-time window. Patients were classified according to the baseline imaging into collateral imaging alone (collateral cohort) and perfusion plus collateral imaging (perfusion cohort). The primary outcome was the proportion of patients achieving independent 90-day functional outcome (modified Rankin Scale 'mRS' 0-2). We used the propensity score-weighting method to balance important predictors between the cohorts. RESULTS: In 608 patients, the median onset/last-known-well to emergency arrival time was 8.8 hours and 53.2% had wake-up strokes. Both cohorts had collateral imaging and 379 (62.3%) had perfusion imaging. Independent functional outcome was achieved in 43.1% overall: 168/379 patients (45.5%) in the perfusion cohort versus 94/214 (43.9%) in the collateral cohort (p=0.71). A logistic regression model adjusting for inverse-probability-weighting showed no difference in 90-day mRS score of 0-2 among the perfusion versus collateral cohorts (adjusted OR 1.05, 95% CI 0.69 to 1.59, p=0.83) or in a favourable shift in 90-day mRS (common adjusted OR 1.01, 95% CI 0.69 to 1.47, p=0.97). CONCLUSION: This pooled analysis of late window EVT showed comparable functional outcomes in patients selected for EVT using collateral imaging alone compared with patients selected using perfusion and collateral imaging. PROSPERO REGISTRATION NUMBER: CRD42020222003.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".