Post–Glucose Load Measures of Insulin Resistance and Prognosis of Nondiabetic Patients With Ischemic Stroke
Bibliographic record
Abstract
Background Insulin resistance is associated with an increased risk of cardiovascular events in the general population. This study aimed to estimate the association between post–glucose load measures of insulin resistance and prognosis of nondiabetic patients with ischemic stroke. Methods and Results Data were derived from the ACROSS ‐China (Abnormal Glucose Regulation in Patients with Acute Stroke across China) registry. Patients with ischemic stroke without a history of diabetes mellitus were included. Two post–glucose load measures of insulin sensitivity, the insulin sensitivity indices ISI (composite) and the ISI 0,120 , were calculated. Outcomes included stroke recurrence, all‐cause death, and poor functional outcome at 12 months. Among 1203 patients, 63.3% were male with an average age of 62.1 years. At 12 months, 168 (14.4%) patients had recurrent stroke, 111 (9.2%) had died, and 288 (24.4%) had poor outcome. After adjustment for potential covariates, the first quartile of the ISI (composite) was associated with increased 12‐month stroke recurrence (adjusted hazard ratio 2.02, 95% CI 1.28–3.18, P =0.003), death (adjusted hazard ratio 2.78, 95% CI 1.59–4.86, P <0.001), and poor outcome (adjusted odds ratio 2.67, 95% CI 1.69–4.21, P <0.001) compared with the fourth quartile. Similar results were observed for the ISI 0,120 but with a larger magnitude of association. Using a multivariable regression model with restricted cubic spline, we found an L‐shaped association between the insulin sensitivity indices and the risk of each end point. Conclusions In this large‐scale registry, post–glucose load measures of insulin resistance with the ISI (composite) and the ISI 0,120 were associated with 12‐month poor outcomes of nondiabetic patients with ischemic stroke.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".