Leptomeningeal collaterals are associated with modifiable metabolic risk factors
Bibliographic record
Abstract
OBJECTIVE: We sought to identify potentially modifiable determinants associated with variability in leptomeningeal collateral status in patients with acute ischemic stroke. METHODS: Data are from the Keimyung Stroke Registry. Consecutive patients with M1 segment middle cerebral artery ± intracranial internal carotid artery occlusions on baseline computed tomographic angiography (CTA) from May 2004 to July 2009 were included. Baseline and follow-up imaging was analyzed blinded to all clinical information. Two raters assessed leptomeningeal collaterals on baseline CTA by consensus, using a previously validated regional leptomeningeal score (rLMC). RESULTS: Baseline characteristics (N = 206) were: mean age = 66.9 ± 11.6 years, median baseline National Institutes of Health Stroke Scale = 14 (interquartile range [IQR] = 11-20), and median time from stroke symptom onset to CTA = 166 minutes (IQR = 96-262). Poor collateral status at baseline (rLMC score = 0-10) was seen in 73 of 206 patients (35.4%). On univariate analyses, patients with poor collateral status at baseline were older; were hypertensive; had higher white blood cell count, blood glucose, D-dimer, and serum uric acid levels; and were more likely to have metabolic syndrome. Multivariate modeling identified metabolic syndrome (odds ratio [OR] = 3.22, 95% confidence interval [CI] = 1.69-6.15, p < 0.001), hyperuricemia (per 1mg/dl serum uric acid; OR = 1.35, 95% CI = 1.12-1.62, p < 0.01), and older age (per 10 years; OR = 1.34, 95% CI = 1.02-1.77, p = 0.03) as independent predictors of poor leptomeningeal collateral status at baseline. INTERPRETATION: Metabolic syndrome, hyperuricemia, and age are associated with poor leptomeningeal collateral status in patients with acute ischemic stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".