Collateral Clock Is More Important Than Time Clock for Tissue Fate
Bibliographic record
Abstract
Background and Purpose— Although perfusion abnormality is an increasingly important therapeutic target, the natural history of tissue at risk without reperfusion treatment is understudied. Our objective was to determine how time affects penumbral salvage and infarct growth in untreated acute ischemic stroke patients and whether collateral status affects this relationship. Methods— We used a prospectively collected, multicenter acute stroke registry to assess acute stroke patients who were not treated with intravenous thrombolysis or endovascular treatment. We analyzed baseline computed tomography angiogram and computed tomography perfusion within 24 hours of stroke onset along with follow-up imaging and assessed time from stroke onset to baseline imaging, ASPECTS (Alberta Stroke Program Early CT Score), vessel occlusion, collaterals, ischemic core, and penumbra. Penumbral salvage and infarct growth were calculated. Correlations between time and penumbral salvage and infarct growth were evaluated with Spearman correlation. Penumbral salvage and infarct growth were compared between subjects with good versus poor collateral status using the Wilcoxon rank-sum test. Clinical and imaging factors affecting penumbral salvage and infarct growth were evaluated by linear regression. Results— Among 94 untreated stroke patients eligible for this analysis, the mean age was 65 years, median National Institutes of Health Stroke Scale score was 13, and median (range) time from stroke onset to baseline imaging was 2.9 (0.4–23) hours. There was no correlation between time and salvaged penumbra ( r =0.06; P =0.56) or infarct growth ( r =−0.05; P =0.61). Infarct growth was higher among those with poor collaterals versus those with good collaterals (median, 52.3 versus 0.9 cm 3 ; P <0.01). Penumbral salvage was lower among those with poor collaterals compared with those with good collaterals (poor, 0 [0–0]; good, 5.9 cm 3 [0–29.4]; P <0.01). Multivariable linear regression demonstrated that collaterals, but not time, were significantly associated with infarct growth and penumbral salvage. Conclusions— In this natural history study, penumbral salvage and infarct growth were less time dependent and more a measure of collateral flow.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".