Prevalence of potentially inappropriate prescriptions in primary care and correlates with mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Potentially inappropriate prescribing is clearly associated with adverse health consequences among older people. Nevertheless, scarce evidence exists regarding the prevalence of potentially inappropriate prescriptions (PIP) in Albania, a Western Balkans country. OBJECTIVE: The aim of this study was to assess the prevalence of PIP among older Albanian patients in primary care and to determine the associated sociodemographic and medical factors, including the presence of mild cognitive impairment (MCI). METHODS: Cross-sectional study in two primary healthcare centers located in two different cities of Albania, a middle-income country in the Western Balkans. The Montreal Cognitive Assessment (MoCA) tool was applied to evaluate MCI. PIPs were assessed by two trained pharmacists using the Beers criteria 2019 update. Multivariate logistic regression analysis was conducted for possible risk factors predicting PIP in the study population. RESULTS: At least one PIP was identified among 40.23 % of the participants (174 older patients) and 10.35 % had more than one PIP. MCI was detected among 79.31 % of the patients. The most commonly represented drug groups in PIP were diuretics (24.71 %), benzodiazepines in the presence of MCI and antidepressants (both 8.62 %). The lack of electrolytes monitoring was the most common reason for PIP. According to the multivariate analysis, the only statistically significant association observed was between PIP and number of drugs prescribed [three to four drugs (OR 3.34; 95% CI 1.65:6.76), five or more than five drugs (OR 4.08; 95% CI 1.42:11.69)]. CONCLUSIONS: About four out of 10 older Albanian patients experience PIP in primary care. Further studies are needed for a comprehensive estimation of the prevalence and factors associated with PIP, particularly among elderly with mild cognitive impairment.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".