Management of schizophrenia: clinical experience with asenapine
Bibliographic record
Abstract
Schizophrenia is a chronic brain disorder comprising a range of clinical features, including positive and negative symptoms, cognitive dysfunction and mood symptoms (particularly depression and anxiety). The management of schizophrenia requires effective short- and long-term treatment with antipsychotic medication that is effective across these symptom domains, while being well tolerated over the long term. Asenapine is the first tetracyclic atypical antipsychotic to be licensed in the USA and several other countries outside Europe for the acute and maintenance treatment of schizophrenia in adults. It has a unique receptor-binding profile and a broad range of therapeutic effects. Since clinical trials are conducted under strict conditions in tightly defined patient populations, evidence of an agent's efficacy and tolerability under 'real-world' clinical practice conditions is also required. As in clinical trials, real-life case reports demonstrate that asenapine is effective in treating the positive symptoms of schizophrenia, both in the acute setting and for relapse prevention. It is also effective in treating negative symptoms and shows promise in the treatment of depressive symptoms associated with schizophrenia. Asenapine has a favourable tolerability profile, having a minimal impact on weight and metabolic parameters. As such, asenapine is valuable option for the treatment of schizophrenia in adults.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".