Medication Mindfulness
Bibliographic record
Abstract
Key Points A medication was deprescribed for approximately one in five eligible patients with an 80% deprescribing success rate. Few significant clinical changes and no related spontaneously reported adverse events indicated a safe and effective intervention. High levels of agreement with algorithm recommendations among clinicians (>80%) and patients (>70%) demonstrated openness to deprescribing. Background Patients on hemodialysis are at increased risk for polypharmacy-related adverse events (AEs). Deprescribing may optimize medication use and mitigate the harmful effects of polypharmacy, but its application in patients on hemodialysis remains understudied. The overall aim of this study was to implement and evaluate the effectiveness and safety of a deprescribing intervention using a deprescribing toolkit in multiple hemodialysis units across Canada. This preliminary study aims to demonstrate the efficacy and safety of the intervention within one hemodialysis unit in Toronto, Canada. Methods This single-center study included patients on hemodialysis for at least three months who were taking at least one of nine study medication classes. Clinicians applied deprescribing algorithms to determine if deprescribing was recommended. Clinicians and patients could decline the algorithm's recommendation. Primary outcomes include the number of patients successfully deprescribed by discontinuing or reducing the dose of their medication over six months and clinically significant AEs. Secondary outcomes include clinician and patient acceptance of algorithm recommendations and clinical monitoring. Results Ninety-eight patients were taking an average of 13.47 (±4.01) medications, with an average of 2.32 (±1.00) being study medications. The algorithms recommended 40 patients to deprescribe 49 study medications. Clinicians agreed to 39 (80%) recommendations, and patients agreed to 28 of those 39 (72%). Twenty patients successfully deprescribed 23 medications (82%), while five patients failed and restarted five medications (18%) at their baseline dose. Clinical monitoring and spontaneous reporting revealed no AEs considered related to the intervention. Conclusions Approximately one in five eligible patients successfully deprescribed a medication with minimal clinical detriment. Although the deprescribing algorithms are valuable in guiding clinical decision making, final decisions rest with clinicians, constituting a careful synthesis of potential benefits, risks, and goals of care for each individual patient. Future research will analyze deprescribing outcomes at additional hemodialysis units in Canada. Clinical Trial registry name and registration number: NCT03733262.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.051 | 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; both teacher heads agree on what is shown here.
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".