#488 Improving medication safety and prescribing of high-risk medications in individuals with chronic kidney disease: a validation study
Bibliographic record
Abstract
Abstract Background and Aims Chronic kidney disease affects 10% of the global population. Medications cleared by the kidneys can accumulate and cause harm if not dosed correctly. Community pharmacists are well-positioned to optimize prescribing for this population, but inconsistencies between medication resources can complicate dosing decisions. Validated algorithms for dosing higher-risk medications in kidney disease are lacking in community pharmacy practice. This study aimed to develop evidence and expert-informed medication algorithms for community pharmacists for individuals with an estimated glomerular filtration rate below 30 ml/min/1.73 m² and validate them for content and face validity. Method Fifty medication algorithms were developed by team experts and revised using Lynn's 3-step method (domain identification, item generation per domain, and instrument formation). For each algorithm, a 2-part questionnaire was administered to two participant groups: first to team experts, followed by community pharmacists. Each group, consisting of 5-6 participants, rated the content and face validity of each algorithm using Likert scales over at least two rounds. The item-level content validity index (I-CVI) and scale-level content validity index (S-CVI/Average) were computed for each medication algorithm per round. To measure face validity, percentages of those that “agreed” or “strongly agreed” to five statements were calculated for both groups. Virtual interviews were conducted and analyzed using qualitative descriptive analysis. Revisions were made to the algorithms between rounds. Results Thirty-eight of the 50 medication algorithms achieved content and face validation by 22 team experts in 2 rounds between August-September 2024. I-CVI and S-CVI/Average ranged from 0.5–1 and 0.83–1 and the overall percentage of participants who agreed or strongly agreed to 5 face validity statements ranged from 50–100%. Thirteen medications were excluded from the community pharmacist's validation. In 2 additional rounds, 23 community pharmacists, with a mean ± standard deviation of 13.74 ± 9.14 years of experience, validated 38 medication algorithms between October-December 2024. I-CVI and S-CVI/Average per medication algorithm ranged from 0.83–1.0 and 0.90–1.0 which met the content validity threshold of 0.83–1.0 (P < 0.05) for at least 5-6 participants per round. Participants overall agreement to face validity statements ranged from 75–100% which was above the prespecified threshold of 70% for face validity consensus. Conclusion Thirty-eight medication algorithms achieved high content and face validity. Future research will integrate these algorithms into an electronic drug dosing and decision support kidney (eDoseCKD) tool and assess their effectiveness and safety in community pharmacy practice in people with chronic kidney disease.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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; 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".