Development and validation of PART (Pharmacotherapy Assessment in Renal Transplant Patients) criteria to assess drug‐related problems in an outpatient renal transplant population: A cross‐sectional study
Bibliographic record
Abstract
Kidney transplant recipients are at risk of pharmacological interactions and adverse drug reactions. Community pharmacists are uniquely poised to detect and intervene in cases of drug-related problems. The aims of this study were to develop and validate a list of explicit criteria to be used by community pharmacists to assess drug-related problems in kidney transplant patients, and to assess their frequency and their determinants. First, we used a modified RAND method where a panel of experts established the PART (Pharmacotherapy Assessment in Renal Transplant Patient) criteria. Then, we performed a cross-sectional study in which we applied the PART criteria to 97 prevalent kidney transplant recipients followed at a single university-affiliated center. The final list of PART criteria included 70 drug-related problems and was reliable (kappa: 0.88). An average of 1.2 drug-related problems per patient was detected when the PART criteria were applied, with 68% of patients having at least 1 problem. This figure was 1.4 per patient using the expert judgment of renal transplant pharmacists who had no access to the PART list. The total number of medications taken was the only factor associated with the number of drug-related problems (β: 0.27 for an increase of five medications, 95% CI 0.005, 0.547). The PART criteria provide a novel tool for community pharmacists to systematically detect drug-related problems in kidney transplant recipients.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".