Potentially inappropriate medication on communitydwelling older adults: Longitudinal analysis using the International Mobility in Aging Study
Bibliographic record
Abstract
Introduction: Medications are a fundamental part of the treatment of multiple pathologies. However, despite their benefits, some are considered potentially inappropriate medications for older people given their safety profile. Epidemiological data differences related to potentially inappropriate medications make it difficult to determine their effects on elderly people. Objective: To estimate the prevalence and types of potentially inappropriate medications using the 2019 Beers Criteria® in a cohort of adults older than 65 years. Materials and methods: We performed an observational, multicenter, retrospective, longitudinal study of a four-year follow-up of potentially inappropriate medications in community-dwelling older adults. Results: We followed 820 participants from five cities for four years (2012-2016) and evaluated them in three different moments (m1 = 2012, m2 = 2014, and m3 = 2016). The average age was 69.07 years, and 50.9% were women. The potentially inappropriate medication prevalence in the participants was 40.24%. The potentially inappropriate medications' mean among the studied subjects in the first moment was 1.65 (SD = 0.963), in the second was 1.73 (SD = 1.032), and in the third was 1.62 (SD = 0.915). There were no statistical differences between measurements (Friedman test, value = 0.204). The most frequent potentially inappropriate medications categories were gastrointestinal (39.4%), analgesics (18.8%), delirium-related drugs (15.4%), benzodiazepines (15.2%), and cardiovascular (14.2%). Conclusions: About half of the population of the community-dwelling older adults had prescriptions of potentially inappropriate medications in a sustained manner and without significant variability over time. Mainly potentially inappropriate medications were gastrointestinal and cardiovascular drugs, analgesics, delirium-related drugs, and benzodiazepines.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".