Nonlinear relationship between glycated hemoglobin and cognitive impairment after acute mild ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Stroke is the second most common cause of morbidity and mortality. Even mild stroke survivors have an increased risk of cognitive impairment. Studies have been conducted on the relationship between glycated hemoglobin (HbA1c) and cognitive decline, but the findings have been inconsistent. Therefore, this study examined the link between HbA1c levels and cognitive impairment following acute mild ischemic stroke. METHODS: Data from 311 patients with acute mild ischemic stroke admitted to Suining Central Hospital, Sichuan Province, China, from January 1, 2015, to December 31, 2018, were evaluated. Fasting venous blood was taken to assess HbA1c levels on the day after admission. Cognitive function was assessed using the Chinese version of the Montreal Cognitive Assessment Scale (MoCA) 3-6 months after stroke onset. We used a generalized additive model and smooth curve fitting (penalty spline method) to assess the nonlinear relationship between HbA1c and poststroke cognitive impairment (PSCI). RESULTS: This study included 311 patients aged 23 to 96 years old (mean age: 67.37 ± 11.92 years), of whom 198 (63.67%) were men. Among the 311 stroke patients, 120 (38.59%) had PSCI. After adjusting for potential confounders, there was a nonlinear relationship between HbA1c and PSCI, with an inflection point of 8.2. To the left of the inflection point, the effect size, 95% confidence interval, and P value were 0.87, 0.58 to 1.31, and 0.5095, respectively; however, to the right of the inflection point, these numbers were 1.96, 1.08 to 3.58, and 0.0280. CONCLUSION: We found a nonlinear relationship between HbA1c and PSCI. When HbA1c was greater than 8.2%, HbA1c was positively correlated with PSCI.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".