Drug prescription patterns and compliance with WHO and beers criteria in older patients
Bibliographic record
Abstract
BACKGROUND: As the population ages, the prevalence of chronic diseases increases, leading to greater reliance on multiple medications that are conducted to increase the risk of adverse drug reactions (ADR) that may cause higher morbidity and mortality rates. This study aims to evaluate medication prescribing patterns in the older adults and assess compliance with the World Health Organization (WHO) guidelines and Beers Criteria. METHODS: A cross-sectional study was conducted over six months in 2022, collecting prescriptions for patients aged 65 and above from a 24-hour community pharmacy in Iran. The prescriptions were analyzed according to the WHO prescribing guidelines, including the mean number of prescribed drugs, the number of injectable drugs and antibiotics per prescription, and also the prescription of drugs with generic names and from the list of Essential Drug List (EDL). In addition, the prescriptions were assessed according to the Beers Criteria for the frequency of prescription of potentially inappropriate medications (PIMs). Also, polypharmacy, which is defined as the prescription of more than five drugs per prescription, has been investigated based on the number of drugs prescribed per prescription. RESULTS: 1,053 older patient prescriptions were assessed, whose average age was 72.3 ± 6.7 years, with 36.2% of prescriptions involving polypharmacy (five or more drugs). The most frequent medical discipline of prescribers was general practice (30.3%). The average number of drugs per prescription was 4.1 ± 2.1, which exceeded the WHO recommendation. Additionally, 47.3% of prescriptions contained at least one PIM according to the Beers Criteria, with non-steroidal anti-inflammatory drugs (NSAIDs) being the most common (17.9%). The relative frequency of injectable drugs and antibiotics used per prescription was 20.8 and 18.9%, respectively, while 7.6% of prescriptions did not use generic names. CONCLUSIONS: The study highlights concern about levels of polypharmacy and PIM use in older patients. While the low rate of antibiotic prescribing and relatively high use of generic drugs indicate some positive adherence to WHO guidelines, the frequent prescription of PIMs and the high average number of drugs per prescription point to substantial room for improvement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".