Potentially inappropriate prescribing among older adults receiving home health care service: A retrospective study in Saudi Arabia
Bibliographic record
Abstract
To address the risks associated with potentially inappropriate prescribing (PIP) in older adults, this study aimed to determine the prevalence of PIP in home health care patients in the Taif Health Cluster, Saudi Arabia. Using the Beers, STOPP, and START criteria, a retrospective analysis was conducted on 400 older adults aged 65 and over who received home health care between February and October 2023. Results indicated that 38.5% of patients had at least one PIP incident, with polypharmacy present in 80.6% of PIP cases. PIP was more prevalent among females and those aged 75 and older. Potentially inappropriate medications (PIMs) were noted in 80.6% of cases, while potentially inappropriate omissions (PIOs) were recorded in 26.5%. The most frequent cause of PIMs was a lack of a clear indication, affecting 23.3% of total prescriptions. Angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor antagonists (AIIRAs) were commonly omitted in diabetic patients with renal disease. Findings highlight the need for regular prescription reviews to reduce PIP and improve patient outcomes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".