Screening Tool for Older Persons’ Appropriate Prescriptions for Japanese: Report of the Japan Geriatrics Society Working Group on “Guidelines for medical treatment and its safety in the elderly”
Bibliographic record
Abstract
AIM: In 2005, the Japan Geriatrics Society published a list of potentially inappropriate medication that was an extract from the "Guidelines for medical treatment and its safety in the elderly 2005." The 2005 guidelines are due for a revision, and a new comprehensive list of potentially inappropriate medications is required. METHODS: A total of 15 diseases, conditions and special areas related to their clinical care were selected. We originated clinical questions and keywords for these 15 areas, carried out a systematic review using these search criteria, and formulated guidelines applying the Grading of Recommendations Assessment, Development and Evaluation system advocated by Minds2014. If we did not find good evidence despite the drug being clinically important, we looked for evidence of efficacy and for disease-specific guidelines, and incorporated them into our guidelines. RESULTS: We selected 2098 articles (140 articles per area), and extracted another 186 articles through a manual search. We further added guidelines based on disease entity and made two lists, one of "drugs to be prescribed with special caution" and the other of "drugs to consider starting," primarily considering individuals aged 75 years or older or those who are frail or in need of special care. CONCLUSIONS: New lists of potentially inappropriate medications and potential prescribing omissions called "Screening Tool for Older Person's Appropriate Prescriptions for Japanese" were constructed. We anticipate that future studies will highlight more evidence regarding the safety of high-quality drugs, further improving the provision of appropriate medical care for the elderly. Geriatr Gerontol Int 2016: 16: 983-1001.
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 | 0.029 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".