A differential detailed diffusion-weighted imaging-ASPECTS for cerebral infarct volume measurement and outcome prediction
Bibliographic record
Abstract
Background: Diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS) has been used to estimate infarct core volume in acute stroke. However, the same and indiscriminate score deduction for punctate or confluent DWI high-intensity lesion might lead to variation in performance. Aims: To develop and evaluate a differential detailed DWI-ASPECTS method in comparison with the conventional DWI-ASPECTS in core infarct volume measurement and clinical outcome prediction. Methods: We retrospectively recruited patients with acute ischemic stroke (AIS) treated with endovascular treatment between April 2013 and October 2019. In differential detailed DWI-ASPECTS, restricted diffusion lesion that was punctate or less than half of a cortical region (M1–M6) would not lead to subtraction of point. A favorable outcome was modified Rankin Scale score ⩽2 at 90 days after stroke onset. Results: Among 298 AIS patients, mean age was 75 years (interquartile range (IQR) 67–82), and 194 patients (65%) were males. Mean infarct core volume was 11 mL (IQR 3–37). Overall, the score by detailed DWI-ASPECTS was significantly higher than conventional DWI-ASPECTS (8 (7–9) vs. 7 (5–9); P < 0.01). The detailed DWI-ASPECTS resulted in a higher correlation coefficient (r) for core infarct volume estimation than the conventional DWI-ASPECTS (r = 0.832 vs. 0.773; P < 0.01). Upon re-classification of those scored ⩽6 in conventional DWI-ASPECTS (n = 134) by detailed DWI-ASPECTS, the rate of favorable outcome in patients with detailed DWI-ASPECTS >6 was significantly higher than those with ⩽6 (29 (48%) vs. 14 (19%); P < 0.01). Conclusions: Detailed DWI-ASPECTS appeared to provide a more accurate infarct core volume measurement and clinical outcome correlation than conventional DWI-ASPECTS among AIS patients treated with endovascular therapy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".