Characteristics and orientation of elderly patients in the largest French psychiatric emergency centre : a prospective cohort study
Bibliographic record
Abstract
Abstract Background Although there is an increasing number of adults older than 60 years old (>60) suffering from psychiatric disorders, there are only few studies about elderly patients in psychiatric emergencies, and no European data. The goal of our study is to describe the population of patients > 60 consulting in the most important French emergency psychiatric centre, and to identify predictive factors of psychiatric hospitalisation. Methods Our study was monocentric and prospective, including 300 consecutive patients > 60. Results Patients > 60 consulting in psychiatric emergencies were more often females and autonomous. More than 40% had a history of at least one psychiatric hospitalisation, and 44% had consulted a psychiatrist in the 6 preceding months. 75% were taking at least 1 psychotropic drug, and 50% at least 2. The most frequent reasons for consultation were depression, anxiety, sleep disorders and suicidal thoughts. Psychiatric disorders were mainly mood disorders, neurotic, stress-related and somatoform disorders, and schizophrenia, schizotypal and delusional disorders. Organic, including symptomatic, mental disorders were diagnosed in only 10% of the total sample. 39% of elderly patients were hospitalised in psychiatry. Factors predicting hospitalisation were a history of psychiatric hospitalisation, suicidal thoughts, and a diagnosis of mood disorder or schizophrenia / schizotypal / delusional disorder. Conclusion Only psychiatric factors intervene in the decision of psychiatric hospitalisation for elderly people consulting in psychiatric emergencies. Socio-demographic characteristics, level of autonomy at home and MMSE score have no influence on the hospitalisation decision. We need more data to better understand the current and future needs of this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".