Measurement Of Hemispheric Volume To Detect And Quantify Early Ischemic Changes In Patients With Ischemic Stroke (P2.121)
Bibliographic record
Abstract
OBJECTIVE: To determine the value of hemispheric volume measurement to quantitate early edema and compare with existing classification scheme. BACKGROUND: Detection and quantification of early ischemic injury on rapidly available computed tomographic (CT) scan can be valuable for selection of patients for intravenous thrombolysis and mechanical thrombectomy. Because of limitations in delineating the ischemic lesion, hemispheric volume asymmetry maybe a better marker for such purposes. METHODS: We analyzed initial CT scans for 28 consecutive ischemic stroke patients who were evaluated within 6 hours of symptom onset and considered for endovascular treatment. We measured hemispheric brain volume for both sides using image analysis software and determine the percentage increase between the two hemispheres. An independent observer classified all the scans based on Alberta Stroke Program Early CT score (ASPECTS) which is a 10-point quantitative CT scan score. We determined the positive predictive value of percentage increase in hemispheric volume for identifying the side of ischemic injury using various thresholds and correlation with ASPECTS. METHODS: The percentage increase in hemispheric volume ranged from 0.1% to 15.9%, with mean [±SD] of 4.16 [±4.01]. The positive predictive value for identifying the side of ischemic injury was 60% for ≤10% and 67% for >10% percentage increase. There was a direct correlation between percentage increase in hemispheric volume and ASPECTS score (correlation coefficient=0.003). In an exploratory analysis, patients with hemispheric ratio greater than 7% on initial CT scan were less likely to have favorable outcome (mRS 0-2) at discharge (p=0.08). CONCLUSIONS: Our study demonstrates the value of quantitating cerebral hemispheric volume on initial CT scan as a sensitive measure to identify extent of early ischemic injury in patients considered for endovascular treatment. However more studies would be needed for validation of this method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".