Antipsychotics in elderly people: to prescribe or to ban?
Bibliographic record
Abstract
INTRODUCTION: The prescription of psychotropic drugs is a major health problem , especially in the elderly. In fact, many studies highlight the misuse of psychotropic drugs and in particular the over-prescription of antipsychotics in the elderly which would be deleterious and not indicated. OBJECTIVES: To evaluate the prescription of antipsychotics in hospitalized elderly people in a psychiatric environment and to compare them with data from the literature. METHODS: This is a retrospective descriptive study of patients aged over 65, hospitalized in the psychiatry department between January 2017 and December 2021 and who received first- or second-generation antipsychotic treatment during their hospitalization. RESULTS: Our sample consisted of 20 patients. More than half of our sample (55%, N=11) had at least one somatic history. More than 20% of subjects, was polymedicated; and for only one patient, the ECG showed an elongation of the space QT counter indicating the use of antipsychotics. The most common diagnosis found was schizophrenia with a rate of 35%,followed by paranoia (20%), and chronic hallucinatory psychosis (15%). More than a quarter of our sample (30%, N=6) received antipsychotic treatment of first generation (AP1G), 10 patients (50%) received antipsychotic treatment of second generation(AP2G) and three patients (15%) received a combination of AP1G and AP2G. More than a quarter of our patients (30%, N=6) reported adverse effects due to neuroleptic treatment. CONCLUSIONS: The results of our study highlighted different indications for which an antipsychotic treatment was prescribed for an elderly person despite a ground often flawed, polymedicated and where the undesirable effects are superimposed. DISCLOSURE OF INTEREST: None Declared
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".