Drug-related problems in elderly general practice patients receiving pharmaceutical care†
Bibliographic record
Abstract
Abstract Objective To describe the types of drug-related problems identified by pharmacists providing pharmaceutical care to elderly patients in the primary care or general medicine setting, and the impact of their recommendations on drug-related outcomes. Methods Searches of the MEDLINE, EMBASE, CINAHL, HealthSTAR, and International Pharmaceutical Abstracts electronic databases from 1990 to 2002 were conducted and a manual search of references from retrieved articles and references on file was performed. Large (n> 100) randomised, controlled studies comparing the provision of pharmaceutical care to usual care in seniors in primary care or general medicine settings were included. Two reviewers evaluated articles based on inclusion criteria and extracted data from the intervention arm of each study, resolving discrepancies by consensus. Nine original articles were included for analysis. Key findings The mean number of drug-related problems (DRPs) identified per patient was 3.2 and the mean number of recommendations made per patient was 3.3. The most common DRP identified was not taking/receiving a prescribed drug appropriately (35.2%, range 4.7–49.3%). The most common recommendations made involved patient education (37.2%, range 4.6–48.2%). Implementation rates were generally high for all types of recommendations, with the highest being for provision of patient education (81.6%). The small number of studies available examining measures of drug utilisation and costs, health services utilisation, and patient outcomes produced inconsistent results, making it difficult to draw conclusions. Conclusions Substantial numbers and a wide range of DRPs were identified by pharmacists who provided pharmaceutical care to seniors in the primary care and general medicine setting. Pharmacists' drug-therapy recommendations were well accepted; however, further study is needed to determine the impact of these recommendations on health-related outcomes.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".