Association between kidney measurements and cognitive performance in patients with ischemic stroke
Bibliographic record
Abstract
Abstract Background Relationships between low estimated glomerular filtration rate (eGFR) and albuminuria with poorer cognitive performance in patients with ischemic stroke are less clear. Our aim was to retrospectively ascertain the associations between these renal measures and cognitive performance in patients with ischemic stroke. Methods Retrospective analysis was performed on 608 patients with acute ischemic stroke. Urine albumin-creatinine ratio (UACR) and eGFR were obtained from inpatient medical records. Global cognitive function via mini-mental state exam (MMSE) and Montreal Cognitive Assessment (MoCA) was determined one month after hospital discharge. The relationship between renal measures and cognitive performance was assessed using univariate and multiple linear regression analyses. Results Patients had an average age of 66.6±4.1 years, and 48% were females. Average eGFR and UACR were 88.4±12.9 ml/min/1.73m 2 and 83.6±314.2 mg/g, respectively. The number of patients with an eGFR of ≥90, 60 to 89, and <60 ml/min/1.73 m 2 were (371, 61%), (207, 34%) and (30, 5%) respectively. The proportions with a UACR <30 mg/g, 30-300mg/g and >300 mg/g were 56%, 39% and 5%. Multivariate adjusted models showed that eGFR was independently associated with MMSE (β= –0.4; 95% CI= –0.5,-0.4; p <0.001) and MoCA (β = –0.6; 95% CI= –0.7,-0.5; p <0.001). However, the correlations between UACR and MMSE and MoCA were statistically non-significant. Conclusion In patients with ischemic stroke, reduced eGFR but not albuminuria was associated with lower cognitive performance. These results show that the eGFR decline could be an effective indicator of cognitive impairment after a stroke. Therefore, regular monitoring and early detection of mild renal dysfunction in patients with acute ischemic stroke might be needed.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".