Recent Advances in the Treatment of Delusional Disorder
Bibliographic record
Abstract
OBJECTIVE: Often considered difficult to treat in the past, even treatment-resistant, delusional disorder is now regarded as a treatable condition that responds to medication in many instances. Munro and Mok previously reviewed the published record of its treatment to 1994. This review aims to update and extend their observations and to examine the impact of new second-generation antipsychotic agents on the treatment of this condition. METHOD: We attempted to gather all published reports of delusional disorder from 1994 to 2004, using various database strategies. We then assessed the reports for clarity and completeness, treatment, and outcome descriptions, thereby selecting a patient sample for analysis. RESULTS: Of 224 cases identified as delusional disorder, only 134 case descriptions provided sufficient treatment and outcome data to inform this review. The demographics of this sample were similar to those of the earlier review. Depression as a comorbid condition was more frequent than before. Adherence to medication regimens was seldom explicitly addressed. Most cases showed improvement regardless of which antipsychotic medication the patients received. Pimozide and other conventional antipsychotics, as well as second-generation antipsychotics, and even clozapine, were used in many of the case reports. Family history of delusional disorder was seldom recorded. CONCLUSIONS: A positive response to medication treatment occurred in nearly 50% of the cases in our review, which is consistent with the earlier review.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".