Inappropriate drug prescriptions among older nursing home residents: the Italian perspective
Bibliographic record
Abstract
Older people take up a large proportion of health care, including drugs, and evidence shows that drug prescribing to this group is often inappropriate. Negative consequences of potential inappropriate drug prescription (PIDP) include adverse drug events, high healthcare service utilization and high costs for the patients and society. Although nursing home residents are the most vulnerable persons exposed to PIDP, few observational studies have investigated the prevalence, the factors associated with and the consequences of PIDP. Epidemiological studies assessing PIDP mainly based on the Beers' criteria showed that approximately half of US and Canadian nursing home residents have at least one PIDP in this setting. The most frequent inappropriate prescriptions concern neuroleptics and long-term benzodiazepines. Nursing home residents aged 80 years or more, those taking a low number of drugs, cognitive or communication problems are less exposed to PIDP compared with residents younger than 80 years, living in facilities with a high number of beds and a lower registered nurse-to-resident ratio. In European countries, the prevalence of PIDP among older nursing home residents was comparable to or higher than that observed in US and Canadian nursing homes. To date, the issue of PIDP has never been investigated in a representative sample of Italian nursing home residents. In a preliminary study performed by our group in 496 nursing home residents randomly selected from 40 nursing homes in Umbria, the prevalence of residents taking at least one or two inappropriate medications was 28% and 7%, respectively. The prevalence of PIDP considering diagnosis (18%) as well as those regardless of diagnosis (17%), as determined by Beers' criteria, were equally distributed in older Italian nursing home residents and no difference was found between sexes. Overall, this review reveals that the prevalence of PIDP is high in both North American and European nursing homes and highlights the urgent need for intervention trials testing strategies to reduce the health and social burden of PIDP.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".