The Association of Glycemic Gap with Cognitive Function After Ischemic Stroke or Transient Ischemic Attack
Bibliographic record
Abstract
Background: Glycemic gap (GG), as a measure of an acute derangement in glucose level in response to an active disease state, has been found to be associated with adverse outcomes in many diseases. This study aimed to determine the relationship of GG with cognitive function after ischemic stroke or transient ischemic attack (TIA). Methods: Patients included were enrolled from a subgroup of China National Stroke Registry-III (CNSR-III). Cognitive function was assessed by the Beijing edition of the Montreal cognitive assessment (MoCA) scale. Post-stroke cognitive impairment (PSCI) was diagnosed as a MoCA score≤ 22. Post-stroke cognitive decline (PSCD) was defined as a decrease of > 2 points on the MoCA score between the 3-month and 1-year assessments. GG was calculated using admission blood glucose minus hemoglobin A1c-derived average blood glucose. Multivariable logistic regression analysis was used to evaluate the correlation between GG and cognitive function. Results: We enrolled 767 patients with a median age of 60 years old, including 247 (32.2%) patients with PSCI in 3 months, 228 (29.73%) with PSCI in 1 year, and 166 (21.64%) patients with PSCD. The highest GG levels were related to PSCI in 3 months after adjusted for multiple potential confounders (adjusted odd ratio (OR): 2.021, 95% CI: 1.055– 3.869, P =0.0338), but not in patients with PSCI in 1 year or PSCD. No significant interactions for the impact on PSCI were observed in subgroups ( P interaction > 0.05 for all). Conclusion: Our findings show that GG is associated with acute post-stroke cognitive impairment, but not with the long-term cognitive impairment or cognitive decline. Keywords: cognitive function, glycemic gap, stroke, transient ischemic attack A Letter to the Editor has been published for this article .
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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) | 0.000 | 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".