Impact of an awareness of the doctors treating on the decrease of the prescriptions of antipsychotics in the demented residents in Ehpad
Bibliographic record
Abstract
BACKGROUND: Despite the context of several national warnings, antipsychotics drugs are commonly used to treat behavioural and psychological symptoms in dementia (BPSD). AIM: To observe a decrease of antipsychotic drug prescription, in old NH (nursing homes) residents with dementia, after an awareness of their general practitioner. METHODS: Observational, prospective, multicenter study. The study population corresponds to NH residents with dementia, and antipsychotic drug consumption, in nursing homes volunteered to participate. Awareness-raising is carried out through information documents. The evaluation criteria is the proportion of residents under antipsychotics after sensitization. RESULTS: out of the 30 nursing homes included, 26.7% of the patients were prescribed at least one antipsychotic and 15% were both demented and under antipsychotics. A total of 317 residents with dementia and antipsychotics were included 15% of the total NH population. Psychotropic drug co-prescriptions was very frequent 43.2% also used benzodiazepines, 37.2% anxiolytics and 33.1% antidepressants. Agitation, aggressiveness, opposition to care and wandering were the most commonly BPSD encountered. After a first sensitization, we obtained a 15.5% decrease of antipsychotic prescriptions. CONCLUSION: A personalized sensitization towards GP allowed a reduction of antipsychotic drugs prescription in NH residents with dementia and BPSD.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".