Attributional styles and other cognitive biases in patients with delusional disorder: A systematic review
Bibliographic record
Abstract
INTRODUCTION: The accurate examination of attributional patterns and cognitive biases in delusional patients is relevant to explain the externalizing tendency in paranoid schizophrenia patients. In subjects with delusional disorder (DD), attributional styles and other cognitive bias have been poorly investigated. OBJECTIVES: Our main goal was to review the tendency to use external-internal attributions for negative events and the presence/absence of other cognitive biases in patients suffering from DD. METHODS: A systematic review was conducted in PubMed and ClinicalTrials.gov databases/registers up to September 2022 according to the PRISMA Guidelines. The following key-words were searched in the title and abstracts: (attributions OR attributional OR “cognitive” OR “cognition” OR “social cognition”) AND (“delusional disorder”). Additionally, references of included studies were manually examined to identify further studies. RESULTS: A total of 144 records were identified (Pubmed, n=125; ClinicalTrials.gov, n=16; other sources, n=13), five studies met our inclusion criteria, reporting attributional styles (n=5) and other cognitive biases (n=2) in DD. (A)Attributional style in DD. Mainly excessive external attributions implying the ascribing of negative experiences to another person’s behavior or action. Other authors describe attributions of negative events to internal causes (n=2). (B)Cognitive biases: Jumping to conclusions bias or judgments made on inadequate evidence have been described in DD (n=2). CONCLUSIONS: Findings in attributional patterns in DD are mixed. Several authors report external and stable attributions in DD, whereas others described internal attributes for negative events, suggesting that depressive vs. “pure” paranoid core dimensions may appear in DD. DISCLOSURE OF INTEREST: None Declared
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".