Topographic distribution of cerebral infarct probability in patients with acute ischemic stroke: mapping of intra-arterial treatment effect.
Bibliographic record
Abstract
BACKGROUND Since proof emerged that IA treatment (IAT) is beneficial for patients with acute ischemic stroke, it has become the standard method of care. Despite these positive results, recovery to functional independence is established in only about one-third of treated patients. The effect of IAT is commonly assessed by functional outcome, whereas its effect on brain tissue salvage is considered a secondary outcome measure (at most). Because patient and treatment selection needs to be improved, understanding the treatment effect on brain tissue salvage is of utmost importance. OBJECTIVE To introduce infarct probability maps to estimate the location and extent of tissue damage based on patient baseline characteristics and treatment type. METHODS Cerebral infarct probability maps were created by combining automatically segmented infarct distributions using follow-up CT images of 281 patients from the MR CLEAN trial. Comparison of infarct probability maps allows visualization and quantification of probable treatment effects. Treatment impact was calculated for 10 Alberta Stroke Program Early CT Score (ASPECTS) and 27 anatomical regions. RESULTS The insular cortex had the highest infarct probability in both control and IAT populations (47.2% and 42.6%, respectively). Comparison showed significant lower infarct probability in 4 ASPECTS and 17 anatomical regions in favor of IAT. Most salvaged tissue was found within the ASPECTS M2 region, which was 8.5% less likely to infarct. CONCLUSIONS Probability maps intuitively visualize the topographic distribution of infarct probability due to treatment, which makes it a promising tool for estimating the effect of treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".