[P2–302]: ONE‐YEAR INTERVAL MONTREAL COGNITIVE ASSESSMENT AND RISK OF PERITONITIS IN SELF‐CARE PERITONEAL DIALYSIS PATIENTS
Bibliographic record
Abstract
Cognitive impairment (CI) may have a negative impact on the outcome of patients on peritoneal dialysis (PD), especially in the risk of peritonitis. This was a two-year prospective single-centre cohort study. Hong Kong Montreal Cognitive Assessment (HK-MoCA) was performed in patients newly started on PD between July 2011 and August 2014, and repeated one year later. Demographics and clinical data including co-morbidities, medications, peritonitis were collected. CI was determined by locally defined HK-MoCA cut-off. Patients were classified as “cognitively impaired” (Ci) if they remained CI or became CI at one-year; remaining patients were classified as “cognitively preserved” (Cp). 104 patients were included. An age older than 65 years old was an independent risk factor for CI (OR 3.37, 95% CI 1.31–8.65, p=0.01). Ci had a higher PD peritonitis rates (1 episode per 22 patient months vs. 1 episode per 54 patient months, p=0.01) and longer median length of admissions (11 days (IQR 6–25) vs. 0 day (IQR 0–6), p<0.001) than Cp after exclusion of helpers. Baseline serum albumin level and classification as Ci were independent factors predicting PD related peritonitis (hazard ratios 0.90 (95% CI 0.82–0.98) p=0.02, 2.48 (95% CI 1.12–5.49) p=0.03 respectively) using cox regression model. Classification as Ci was associated with shorter peritonitis free survival according to Kaplan Meier curve (p=0.01). Age is an independent risk factor for CI in PD patients. CI increases the risk of PD-related peritonitis.
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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) | 0.001 | 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 | 0.000 |
| 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".