Prevalence of self‐reported risk factors for medication misadventure among older people in general practice
Bibliographic record
Abstract
OBJECTIVE: To describe the prevalence of risk factors for medication misadventures among older people in general practice. DESIGN: Descriptive cross-sectional analysis. SETTING: General practices, New South Wales, Australia. PARTICIPANTS: Twenty general practitioners in 16 practices recruited 849 practice attendees aged 65 years and over. OUTCOME MEASURE: Risk factors for medication misadventures. RESULTS: Almost all participants (95%) had used at least one medication for more than 6 months. More than half of the participants had more than one doctor involved in their care (59%), had three or more health conditions (57%), or used five or more medicines (54%). With regard to potential adverse drug reactions, in the last month 39% of participants experienced difficulties sleeping, one-third felt drowsy or dizzy (34%), and about a quarter had a skin rash (28%), leaked urine (27%), had stomach problems (22%) or had been constipated (22%). The most common compliance problems were experiencing side effects (14%) and having difficulties opening bottles or packets/applying the medicine (10%). CONCLUSION: Risk factors for medication misadventure remain a substantial problem among older people. A Medication Risk Assessment Form completed by patients can be used as an aid to increase general practitioners' awareness of a variety of problem areas associated with medication use in a compact way, and could be used as part of a system for medication review to determine whether actions are required to improve quality use of medicines.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".