Factors associated with potentially inappropriate prescribing in elderly patients with various degrees of chronic kidney disease
Bibliographic record
Abstract
INTRODUCTION: This study aimed to compare the prevalence of potentially inappropriately prescribed drugs in hemodialysis patients and patients with chronic kidney disease who did not require renal replacement therapy, as well as to identify risk factors associated with potentially inappropriate prescribing. METHODS: The study was designed as a cross-sectional study conducted at the Department of Nephrology, Clinical Center in Nis, Serbia. The patients were divided into two groups: (1) patients on hemodialysis treatment and (2) patients with various degrees of chronic kidney disease without renal replacement therapy. The presence or absence of potentially inappropriate prescribing was determined using the 2015 AGS Beers criteria. FINDINGS: The study included a total of 218 patients aged 65 years and over. The number of patients with potentially inappropriate prescribed drugs did not differ significantly (chi-square = 0.000, p = 1.000) between patients on hemodialysis (27 of 83, i.e., 32.5%) and patients with various degrees of chronic kidney disease without renal replacement therapy (44 of 135, i.e., 32.6%). Factors associated with potentially inappropriate prescribing in hemodialysis patients were the number of drugs (hazard ratio [HR] = 1.919, 95% confidence interval [CI]: 1.325-2.780) and number of comorbidities (HR = 1.743, 95% CI: 1.109-2.740). The number of drugs (HR = 1.438, 95% CI: 1.191-1.736) was the only independent factor associated with increased risk of potentially inappropriate prescribing in patients without renal replacement therapy. DISCUSSION: Our study showed that potentially inappropriate prescribing is a relatively frequent phenomenon present in about a third of patients in both study groups. The number of prescribed drugs was the main factor associated with the increased risk of potentially inappropriate prescribing in both groups.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".