Improving medication safety and prescribing of higher-risk medications in individuals with chronic kidney disease: A validation study
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: Chronic kidney disease (CKD) affects 1 in 10 Canadians. Medications cleared by the kidneys can be harmful if dosed improperly. Community pharmacists are well-positioned to optimize prescribing, but inconsistencies between medication resources can complicate dosing. This study developed and validated higher-risk medication toolkits, including decision support algorithms for community pharmacists managing people with CKD. Methods: Fifty-one toolkits and algorithms were developed by team experts using Lynn’s method (domain identification, item generation per domain, and instrument formation). Team experts followed by community pharmacists rated toolkit content and algorithm face validity using a 2-part questionnaire with Likert scales. Each toolkit was validated by 5 to 6 participants over 2 rounds. Content validity was computed using an item-level content validity index (I-CVI) and scale-level content validity index (S-CVI/Ave) per round. Face validity calculated percentages for level of agreement to 5 statements. Community pharmacist interviews were conducted after each round, data analyzed, and toolkit revisions were made between rounds. Results: Twenty-two team experts validated 51 toolkits in 2 rounds between August and September 2024. Toolkit I-CVI, S-CVI/Ave, and face validity per algorithm ranged from 0.5 to 1, 0.87 to 1, and 49% to 100%, respectively. Thirteen toolkits were excluded from the community pharmacist validation. In 2 additional rounds, 23 community pharmacists, with 13.7 ± 9.1 years of experience, validated 38 medication toolkits between October and December 2024. Toolkit I-CVI and S-CVI/Ave and face validity per algorithm ranged from 0.83 to 1 and from 0.87 to 1, which met the content validity threshold of 0.83 to 1 ( P < 0.05) for at least 5 to 6 participants per round. Participants’ overall agreement for the face validity statements ranged from 75% to 100%, which was above the prespecified threshold of 70% for face validity consensus. Conclusions: Thirty-eight toolkits achieved high content and face validity. Future research will integrate them into a digital tool and assess their effectiveness and safety in community pharmacy practice in people with CKD.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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