New models of chronic kidney disease care including pharmacists
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with chronic kidney disease (CKD) are complex, have many medication-related problems (MRPs) and high rates of medication nonadherence, and are less adherent to some medications than patients with higher levels of kidney function. Nonadherence in CKD patients increases the odds of uncontrolled hypertension, which can increase the risk of CKD progression. This review discusses reasons for gaps in medication-related care for CKD patients, pharmacy services to reduce these gaps and successful models that incorporate pharmacist care. RECENT FINDINGS: Pharmacists are currently being trained to deliver patient-centred care, including identification and management of MRPs and helping patients overcome barriers to improve medication adherence. A growing body of evidence indicates that pharmacist services for CKD patients, including medication reconciliation and medication therapy management, positively affect clinical and cost outcomes, including lower rates of decline in glomerular filtration rates, reduced mortality and fewer hospitalizations and hospital days, but more robust research is needed. Team-based models including pharmacists exist today and are being studied in a wide range of innovative care and reimbursement models. SUMMARY: Opportunities are growing to include pharmacists as integral members of CKD and dialysis healthcare teams to reduce MRPs, increase medication adherence and improve patient outcomes.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".