Potentially inappropriate medication use at ambulatory care visits by elderly patients covered by National Health Insurance in Korea
Bibliographic record
Abstract
OBJECTIVES: Potentially inappropriate medication (PIM) use is an important and preventable safety concern in the care of elderly patients and has been associated with adverse drug reactions, hospitalization, and mortality. Although PIM use for the elderly is a common and serious public health issue worldwide, there are few studies examining PIM use in the ambulatory care setting in Korea. METHODS: To examine the prevalence and risk factors of PIM use from ambulatory care visits by elderly patients covered by National Health Insurance (NHI) in Korea, the nationwide prescription claims data of elderly patients' ambulatory care visits in 2006 were analyzed. RESULTS: Potentially inappropriate prescriptions were identified using extensive criteria that included Beers', Zhan's, and Canadian criteria. In 2006, 3,770,978 elderly patients received 40,995,267 prescriptions. 36.7% of the total prescriptions for elderly patients who visited ambulatory care clinics were identified as PIM use. Findings in this study indicated that the strongest risk factors for PIM prescriptions were the number of drugs prescribed and visit characteristics. CONCLUSION: Therefore, it is necessary to develop the explicit criteria of PIM prescription in Korea that can be included in the Drug Utilization Review (DUR) system, which is expected to lead to more appropriate and judicious prescribing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".