Development of a list of potentially inappropriate medication for the Chinese aged people
Bibliographic record
Abstract
Objective To develop a list of potentially inappropriate medication(PIM) for the Chinese aged people and provide reference for prevention and reduce the medication risk of the aged people. Methods Based on the PIM lists of the United States, Canada, Japan, France, Norway, Germany, South Korea and Austria, and combined with the data of serious adverse drug reactions(ADR)in the aged people collected from China National Center for ADR Monitoring, ADR monitoring center in the People's Liberation Army, Beijing Center for ADR Monitoring and ADR data from Beijing 22 hospitals, we created a preliminary PIM list for the Chinese aged people. Using Delphi technique experts consultation was made for the initial list. Round 1 consultantation invited 32 experts, according to the expert advice to adjust the initial list, and form a revised list. Round 2 consultantation invited 38 experts, according to the expert advice to adjust revised list, and the final version of the PIM list formed. Results A total of 13-class 72 medications or medication classes were selected as the Chinese aged people PIM list, each medicine had 1-6 risk points. The list was divided according to the result of expert evaluation into 35 kinds of high risk medications and 37 kinds of low risk medications. In addition, according to the frequency of drug use, the medications were divided into A and B two categories, including 24 medications or medication classes as the preferred alert medications(A), 48 medications or medication classes as routinely alert medications(B). Conclusion PIM list for the Chinese aged people have been developed, which can be taken as reference to intervention and evaluation of China's elderly medication. Key words: Aged; Catalogs, drug; Delphi technique; Potentially inappropriate medication; Beers criteria
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".