Deprescribing in chronic kidney disease: An essential component of comprehensive medication management
Bibliographic record
Abstract
Chronic kidney disease (CKD) is categorized by abnormalities of kidney structure or a sustained reduction (for greater than 3 months) in estimated glomerular filtration rate to less than 60 mL/min/1.73 m2 and/or 2 of 3 urine albumin creatinine ratio measures of 30 mg/g (3 mmol/L) or higher.1 The prevalence of CKD in the US and Canada is 14% (35.5 million) and 12.5% (4 million), respectively.2-8 This corresponds to an estimated 1 in 7 Americans and 1 in 10 Canadians with CKD.2-8 Individuals with advanced CKD receiving kidney replacement therapy have a high medication burden, taking a mean (SD) of 12 (5) medications per day.9-12 Multiple comorbidities, advanced age, and polypharmacy are common in individuals with CKD.9-14 Polypharmacy refers to taking 5 or more medications on a regular basis as well as any inappropriate choices and doses of medications.15 Approximately 70% to 80% of individuals with CKD are prescribed 5 or more medications13,16 and receive a mean (SD) of 5.37 (2.83) potentially inappropriate medications (PIMs).17 Given the potential for adverse consequences associated with polypharmacy, ongoing assessment of medications is crucial. This review aims to highlight the consequences of polypharmacy in individuals with CKD, including those with end-stage kidney disease (ESKD), and provide medication optimization strategies, using deprescribing approaches to enhance medication management.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".