Joint effects of elevated homocysteine levels and low eGFR on post-stroke cognitive impairment
Bibliographic record
Abstract
Introduction The correlation between serum homocysteine levels and post-stroke cognitive impairment (PSCI) remains inconsistent. This study aimed to investigate whether serum homocysteine levels are independently associated with PSCI and to assess the effects of renal function on this relationship. Methods A retrospective analysis was conducted in 608 patients with ischemic stroke. Homocysteine levels were obtained from inpatient medical records, and global cognitive function status 1 month after discharge was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The relationship between homocysteine levels and PSCI was evaluated using univariate and multiple linear and logistic regression analyses. Results The mean age of the patients was 66.6 ± 4.1 years, with 48% being female. The median homocysteine level was 13.8 μmol/L (interquartile range [IQR], 11.3–17.3 μmol/L), and 39.3% of patients had total homocysteine levels above the cutoff of 15 μmol/L. After full adjustment, a stronger positive association between homocysteine levels and PSCI was observed in patients with low estimated glomerular filtration rate (eGFR), with significant interactions between eGFR and MMSE scores (P for interaction = 0.005) and between eGFR and MoCA scores (P for interaction = 0.001). Joint analyses indicated that the highest risk of PSCI was in patients with eGFR < 90 ml/min/1.73 m2 and homocysteine levels ≥15 μmol/L (odds ratios [ORs] were 2.50 [95% CI: 1.49, 4.18; p < 0.001] for MMSE and 13.53 [95% CI: 6.64, 27.56; p < 0.001] for MoCA in the fully adjusted model). Conclusion These findings highlight the additive value of hyperhomocysteinemia and lower eGFR in predicting incident PSCI risk.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".