Pharmacological management of comorbid obsessive–compulsive disorder and chronic non-affective psychosis
Bibliographic record
Abstract
SUMMARY The comorbidity of obsessive–compulsive symptoms (OCS) in the context of schizophrenia is often not recognised by clinicians, and patients may not report these symptoms until they become severe. However, there is a reported prevalence of 10–24% for obsessive–compulsive disorder (OCD) in schizophrenia and related disorders. The onset of OCS/OCD has been noted to occur both before and after the diagnosis of schizophrenia or schizoaffective disorder. It has also been known to occur following commencement of treatment with antipsychotic medications, especially clozapine. Current literature provides limited guidance for treatment. Review of the current evidence supports: addition of selective serotonin reuptake inhibitors (SSRIs) to antipsychotics; addition of aripiprazole, amisulpride or lamotrigine; or reduction in the dosage of clozapine. There is also evidence supporting the addition of cognitive–behavioural therapy and electroconvulsive therapy (ECT). The SSRIs that are evidenced to be useful are fluvoxamine, escitalopram, sertraline and paroxetine. More studies are needed to expand the evidence base. Early targeted interventions are recommended.
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) | 0.005 | 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".