Treating Delusional Disorder: A Comparison of Cognitive-Behavioural Therapy and Attention Placebo Control
Bibliographic record
Abstract
OBJECTIVE: Cognitive-behavioural therapy (CBT) has proved effective in treating delusions, both in schizophrenia and delusional disorder (DD). Clinical trials of DD have mostly compared CBT with either treatment as usual, no treatment, or a wait-list control. This current study aimed to assess patients with DD who received CBT, compared with an attention placebo control (APC) group. METHOD: Twenty-four individuals with DD were randomly allocated into either CBT or APC groups for a 24-week treatment period. Patients were diagnosed on the basis of structured clinical interviews for mental disorders and the Maudsley Assessment of Delusion Schedule (MADS). RESULTS: Completers in both groups (n = 11 for CBT; n = 6 for APC) showed clinical improvement on the MADS dimensions of Strength of Conviction, Insight, Preoccupation, Systematization, Affect Relating to Belief, Belief Maintenance Factors, and Idiosyncrasy of Belief. CONCLUSION: When compared with APC, CBT produced more impact on the MADS dimensions for Affect Relating to Belief, Strength of Conviction, and Positive Actions on Beliefs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".