Cognitive impairment in patients with moderate to severe CKD: The Salford Kidney Cohort Study
Bibliographic record
Abstract
Background Cognitive Impairment (CI) in chronic kidney disease (CKD) is common and underrecognized [1,2]. Determining risk factors for CI and whether speed of CKD progression is an important consideration may help identification of CI by nephrologists. Vascular disease is thought to underpin CI in CKD and by segregating CKD patients with proven vascular disease, we may also be able to discover other important associations with CI in CKD patients. Method 250 patients in a UK prospective cohort of CKD patients underwent 2 cognitive assessments; Montreal Cognitive Assessments and Trail Making Tests. CI was defined using validated population cut offs (CI) and relative cognitive impairment (rCI). rCI was defined by < 1SD below mean Z score on any completed test. Two multivariable logistical regression models identified variables associated with CI and rCI. Results 44% and 24.8% of patients suffered CI and rCI respectively. Depression, previous stroke and older age were significantly associated with CI. Older age was significantly associated with rCI (p=<0.05), higher proteinuria and use of psychodynamic medications were also significantly associated with rCI (p=0.05). Delta eGFR in patients with CI and rCI compared with those having normal cognition was similar (-0.77 versus -1.35 mL/min/1.73m2/yr p=0.34 for CI and -1.12 versus -1.02 mL/min/1.73m2/yr p=0.89 for rCI). Conclusion Risk factors for CI in CKD include; previous stroke, depression or anxiety, higher proteinuria and prescription of psychodynamic medications. Patients with a faster eGFR decline do not represent a group of patients at increased risk of CI.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".