Bibliographic record
Abstract
Depressive symptoms during long-term course of schizophrenia constitute an important and frequent clinical problem. They may occur either as stand-alone major depressive episodes (MDEs) or as part of the schizophrenic negative syndrome. Teatment resistant schizophrenia due to affective deficits results in high subjective burden of disease and a marked subgroup of schizophrenia patients die from suicide. International treatment guidelines strongly suggest offering cognitive behavioural therapy to all patients with schizophrenia. Within pharmacological approaches evidence in favour of second generation antipsychotics exist. The application of mood stabilizers lacks evidence from clinical trials, but is often used in clinical practice. Several antidepressive agents have been administered to depressed patients with schizophrenia and were effective in alleviating both affective and negative symptoms. Treatment outcomes, however, were often limited by side effects and pharmacokinetic interactions, which constitutes the necessity of more easily tolerable pharmacological interventions. Data regarding duloxetine, bupropion, vortioxetine and agomelatine are presented in more detail and discussed within the perspective of multimodal treatment of schizophrenia. Disclosure M.Z. received scientific grants from the German Research Foundation and Servier. Speaker and travel grants were provided from Otsuka, Servier, Lundbeck, Roche, Ferrer and Trommsdorff.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".