A review on schizophrenia and relapse—a quest for user‐friendly psychopharmacotherapy
Bibliographic record
Abstract
OBJECTIVE: Schizophrenia in general is notoriously associated with relapses rendering the illness progressive to worse outcomes, a concept of which is compatible with neurotoxicity. Therefore, relapse prevention is of utmost clinical relevance. METHODS: In this review, we aim to put relapse into clinical context in the realm of natural history of, or heterogeneity in, schizophrenia and summarize risk factors of relapse. We discuss how to effectively 'define' relapse in schizophrenia and recent meta-analytic studies on this topic to highlight the importance of continuous antipsychotic treatment. RESULTS: The following issues emerged: 'How low maintenance antipsychotic dosage could be?’, 'How extended dosing could be?’, 'Who could be successfully withdrawn from antipsychotics?’ and 'How relapse could be defined in the first place?’ The question in particular is how better to deliver antipsychotics at the lowest possible, whereby dose and dosing interval are relevant. While ongoing antipsychotic treatment is the rule, recent works are pointing to a possibility of lower dosage in the maintenance phase of the illness. CONCLUSIONS: Bearing in mind that suboptimal adherence and withdrawal from antipsychotics are an established and unequivocal risk factor for relapse, further investigations are certainly needed to explore user-friendly manner of psychopharmacotherapy to prevent relapse in schizophrenia.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 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".