2373 – Forensic Psychiatric Outpatient Consultation Service At a General Hospital Psychiatric Department Setting In Budapest. Five-year Experience: Conclusions, Further Suggestions
Bibliographic record
Abstract
Introduction Due to the unprofessional closure of the Hungarian National Institute of Psychiatry and Neurology at 2007, the forensic psychiatric outpatient consultation service of the Main Detention Centre of Budapest (MDC), was officially transferred to the Psychiatric Department of Saint John Hospital. Hungary has had neither a Forensic Psychiatric Unit nor a single Security Units Department. At our psychiatric department first we started operating this type of consultation. in 2007 May. Before our consultations the clients are examined by the medical doctor of the MDC, and his/her decision to ask for psychiatric examination with specific questions. We analysed retrospectively these cases of 5 years. Objectives Statistical and qualitative analysis of clients’ reports of 346. The clients were before judicial process and detained in custody or before it in the MDC. Aims To analyse the frequent problems and questions, the necessity of these problems, and severity of the clients symptoms. Methods We analysed all the files, and the objectives: referral diagnosis, diagnosis after our examination, suicide intentions, intentional aggravation, heteroagression, psychiatric history, drug use and dependency, current medication, presence of psychotic symptoms. Results Between 2007–2009 August there were 67 examinations, and between 2009 September and 2012 were 280 visits. The most frequent question was to evaluate the suicide risk. (three-quarter of the sample.) Conclusion Specialised service is needed (Forensic Unit) to evaluate more precisely the severity of suicide risk, and to exclude the intentional behaviour. External causes may influence the higher number of the cases, after 2009 August.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".