Drug-related problems in the elderly - Interventions to improve the quality of pharmacotherapy
Bibliographic record
Abstract
Introduction: Elderly people, in particular those residing in nursing homes, often use many drugs. In general elderly patients are at greater risk of experiencing drug-related problems (DRP), as they have multiple diseases, are using many drugs and have changed physiological status. Objectives: To describe the frequency of potential drug-related problems in the elderly and to evaluate different kinds of interventions that are meant to reduce the number of potential drug-related problems in the elderly. Methods: (Paper I) All information on medication use in nursing home patients with epilepsy or Parkinson's disease was collected. A multi-speciality team evaluated nursing home patients' medication and, when appropriate, suggested changes. (Paper II) Elderly patients that had been discharged from hospital were identified. All information on their medications prior to, during and after hospital care was collected. Medication errors during transfer between care levels were identified. (Paper III) Educational outreach visits were offered General Practitioner (GP) practices. Data on prescribing of benzodiazepines and anti-psychotic drugs to elderly before and after this education was compared with a control group of GP practices. (Paper IV) Implementation of a medication report when elderly patients are discharged from hospital care. Results: Inappropriate medications are common in nursing homes. Medication errors are frequent when elderly patients are transferred between hospital and primary care (Papers II and IV). Advice from a multi-speciality team did not have any positive effects on the quality of life in nursing home patients (Paper I). Educational outreach visits are well appreciated by GPs and can affect their prescribing habits leading to a decrease in prescribing of inappropriate medications to elderly patients (Paper III). Medication Report is effective in reducing the number of medication errors when elderly patients are transferred from hospital to primary care (Paper IV). Conclusions: The research comprising this thesis has demonstrated a need for attention towards drug-related problems in the elderly. Educational outreach visits are effective in affecting GPs prescribing habits. Medication report is a simple but very effective instrument to decrease the number of medication errors when elderly patients are discharged from hospital.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".