Ethno-psychopharmacological aspects of treatment response in patients with delusional syndrome: A systematic review
Bibliographic record
Abstract
Introduction Treatment response in schizophrenia can be influenced by cultural and ethno-biological factors. However, in delusional disorder (DD), these potential influences have been poorly investigated. Objectives This review aims to synthesize what is known about the influence that cultural and biological factors may have on treatment response in DD. Methods A systematic review was performed on PubMed from inception to 2020 in keeping with PRISMA directives. Search terms: [(cultural OR ethnic* OR ethno*) AND (treatment OR therap* OR antipsychotic response) AND (delusional disorder)]. We included all studies whose objective was to explore ethno-psychopharmacological aspects of treatment response in DD. Results A total of 182 papers were retrieved. Four studies tested ethno-biological factors and 10 reported cultural aspects of treatment response in DD. 1. Cultural hypothesis: 3 studies reported cultural differences in diagnostic practices; in 2 studies, culturally-determined long durations of untreated psychosis (DUP) and comorbidity with mood disorders was associated with response to both antipsychotics (AP) and antidepressants (AD); 3 studies reported that response and AP dose were similar among cultures and that culturally-sensitive psychotherapy improved adherence; 2 studies showed that, where women had poor access to health care, mortality rates were high. 2. Ethno-biological hypothesis: 1 study reviewed moderators and mediators of ethno-specific treatment response; 1 study presented a culture-bound syndrome (Taijin kyofusho) for which AD were found effective; 2 studies in diverse populations found that DD and schizophrenia were both significantly linked to HLA genes. Conclusions The sociodemographic profile of DD is consistent across various cultures and, when treated appropriately, responds, but in an ethno-culturally-specific manner. Disclosure No significant relationships.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".