Triglyceride-glucose index and its association with collaterals in acute ischemic stroke: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Leptomeningeal collaterals (LMCs) carry important prognostic value for patients with Acute ischemic stroke (AIS). Insulin resistance (IR) is associated with cardiovascular disease, but its impact on stroke outcomes is less clear. The triglyceride-glucose (TyG) index is a novel, accessible biomarker for IR. This study aims to evaluate the association between TyG index and LMCs. METHODS: In this cross-sectional study, we included patients with internal carotid artery or middle cerebral artery occlusion and having CTA within 24 h of symptoms onset. TyG index formula was: ln [triglyceride (mg/dl) × glucose (mg/dl)]/2. CTA collaterals were classified into symmetrical, malignant, and other. Hypoperfusion Intensity Ratio (HIR) calculated from CT perfusion (CTP) was used as secondary marker. We did a multivariate ordinal logistic regression analysis for collaterals and multi linear regression analysis for HIR. RESULTS: Of 265 patients, 54.1 % were males, and 45.9 % were females, with a mean BMI of 28. A total of 219 (82 %) patients received reperfusion therapy, with 40 % and 74 % patients receiving tPA and EVT, respectively. 39.2 % and 27.2 % of patients had symmetrical and malignant CTA collateral scores, respectively. TyG index was a significant predictor of malignant CTA collaterals (β = 2.2, 95 % CI: 1.7 - 2.7) and CTP HIR (β = 0.1, 95 % CI: 0.03-0.14). TyG > 8.7 had diagnostic accuracy of 0.76 for malignant CTA collaterals. CONCLUSIONS: IR, as measured by TyG index, is predictive of worse LMCs. TyG is a promising biomarker with a potential value in predicting stroke outcomes after reperfusion therapy.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".