An Evaluation of Medication Reconciliation in an Outpatient Nephrology Clinic.
Bibliographic record
Abstract
Background: Accreditation Canada recognizes medication reconciliation as a key required organizational practice (ROP) to enhance patient safety. Patients with chronic kidney disease (CKD) carry a high risk for adverse drug events due to multiple co-morbidities, using many medications, and being cared for by many practitioners. Data evaluating the benefits of ambulatory medication reconciliation (AmbMR) in patients with advanced CKD is limited. Methods: We retrospectively evaluated types and rates of medication discrepancies and their potential index for patient harm using the Cornish classification system in a cohort of consecutive non-dialysis-dependent CKD stage 5 patients who received AmbMR. Results: AmbMR was conducted 225 times on 115 patients during the study period. One hundred eighty medication discrepancies were identified. The most common discrepancy identified was incorrect drug followed by discrepant dose, discrepant frequency, and drug omission. Sixty-three percent of discrepancies were classified as unlikely to cause patient discomfort or clinical deterioration, 36% were classified as likely to cause moderate harm, and one percent was classified as potential to cause serious harm. Conclusion: Medication discrepancies are common in patients with advanced CKD. Nearly a quarter of patients may experience moderate discomfort or clinical deterioration from discrepancies. Our study showed that in patients with non-dialysis-dependent CKD stage 5, the risk of patient harm associated with medication discrepancies can be reduced by conducting AmbMR.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".