Development and Validation of Patient Education Tools for Deprescribing in Patients on Hemodialysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Deprescribing is a patient-centered solution to reducing polypharmacy in patients on hemodialysis (HD). In a deprescribing pilot study, patients were hesitant to participate due to limited understanding of their own medications and their unfamiliarity with the concept of deprescribing. Therefore, patient education materials designed to address these knowledge gaps can overcome barriers to shared decision-making and reduce hesitancy regarding deprescribing. Objective: To develop and validate a medication-specific, patient education toolkit (bulletin and video) that will supplement an upcoming nationwide deprescribing program for patients on HD. Methods: Patient education tools were developed based on the content of previously validated deprescribing algorithms and literature searches for patients' preferences in education. A preliminary round of validation was completed by 5 clinicians to provide feedback on the accuracy and clarity of the education tools. Then, 3 validation rounds were completed by patients on HD across 3 sites in Vancouver, Winnipeg, and Toronto. Content and face validity were evaluated on a 4-point and 5-point Likert scale, respectively. The content validity index (CVI) score was calculated after each round, and revisions were made based on patient feedback. Results: A total of 105 patients participated in the validation. All 10 education tools achieved content and face validity after 3 rounds. The CVI score was 1.0 for most of the tools, with 0.95 being the lowest value. Face validity ranged from 72% to 100%, with majority scoring above 90%. Conclusion: Ten patient education tools on deprescribing were developed and validated by patients on HD. These validated, medication-specific education tools are the first of its kind for patients on HD and will be used in a nationwide implementation study alongside the validated deprescribing algorithms developed by our research group.
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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 | 0.001 | 0.018 |
| 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) | 0.000 | 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 it