Bibliographic record
Abstract
Clinical features of post-psychotic depression in schizophrenia have been described since the beginning of the century. However, international nosographies mention this concept only since the ICD 10 and the DSM IV. In clinical practice, post-psychotic depression is a real challenge. Currently, the exact prevalence remains undetermined and is estimated about 25%, varying from 7 to 70% in the literature. The diagnostic criteria nowadays available will encourage searchers to determine the exact prevalence of post-psychotic depression. This is surely due to difficulties in the diagnostic approach. The clinical picture resembles that of major depression. However, there are confounding factors such as negative symptoms and extrapyramidal symptoms. With regard to psychometrics, two specific rating scales are thought to measure depressive symptoms in schizophrenia: the Calgary Depression Scale (CDS) and the Psychotic Depression Scale (PDS). Nonetheless, the scales are not specific for post-psychotic depression. Prognosis of an acute schizophrenia is linked among other factors with the emergence of a post-psychotic depression that is in turn influences suicidal risk and quality of life. Genetic, therapeutic, psychodynamic and psychological factors have been invoked in the etiopathogenesis of post-psychotic depression. In clinical practice, post-psychotic depression can be successfully treated with antidepressive medication. Some antidepressants have shown their efficacy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.015 |
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".