Health Survey Data on Potentially Inappropriate Geriatric Drug Use
Bibliographic record
Abstract
BACKGROUND: Studies have suggested that elderly patients do not always use medications appropriately. Investigations that have relied on prescription claim databases or clinical records focus on acquisition or prescription, and hypotheses must be made to assess actual consumption. Population survey data constitute an alternative way to study inappropriate use. OBJECTIVE: To estimate the prevalence of potentially inappropriate use of medications in elderly patients in Québec based on self-reported use. METHODS: Using a cross-sectional, general population, health survey in which self-reported medication use in the 2 days prior to the survey was recorded, we estimated the prevalence of inappropriate medication use in elderly patients (≥65 y old) who responded. Two sets of published criteria were used to define inappropriate use: one to assess use of inappropriate drugs, and another to assess concomitant duplications and potential interactions. RESULTS: Of the 3400 patients surveyed, 6.5% had used ≥1 inappropriate drugs, 2.5% had ≥1 occurrences of potentially inappropriate duplication of medications, and 2.7% had ≥1 potential medication interactions. Concomitant use of at least 2 benzodiazepines was reported by 8.5% of respondents using these drugs. Use of ≥1 long-acting benzodiazepines was reported by 4.2% of the sample. CONCLUSIONS: Population health surveys are a useful tool for detecting potentially inappropriate medication use in the elderly. In particular, the high prevalence of inappropriate use of benzodiazepines signals a need for improved detection and intervention in this group. TRANSFONDO: Existen estudios que demuestran que los ciudadanos viejos no siempre usan sus medicamentos adecuadamente. Estudios basados en los bancos de datos de los reclamos de prescripción o expedientes clínicos se hacen a base de la adquisición o prescripción de medicamentos. De estos se crean hipótesis para estimar el consumo real de medicamentos. Una manera alterna de realizar estudios sobre el uso inapropiado de medicamentos lo es a través de datos obtenidos de encuestas poblacionales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".