Collaboration between Psychiatrists and Other Allied Medical Specialists for the Treatment of Delusional Disorders
Bibliographic record
Abstract
BACKGROUND: There is increasing evidence that individuals with psychosis are at increased risk for cardiovascular disease, diabetes, metabolic syndrome, and several other medical comorbidities. In delusional disorder (DD), this is particularly so because of the relatively late onset age. AIMS: The aim of this narrative review is to synthesize the literature on the necessity for medical collaboration between psychiatrists and other specialists. METHODS: A non-systematic narrative review was carried out of papers addressing referrals and cooperation among specialists in the care of DD patients. RESULTS: Psychiatrists, the primary care providers for DD patients, depend on neurology to assess cognitive defects and rule out organic sources of delusions. Neurologists rely on psychiatry to help with patient adherence to treatment and the management of psychotropic drug side effects. Psychiatrists require ophthalmology/otolaryngology to treat sensory deficits that often precede delusions; reciprocally, psychiatric consults can help in instances of functional sensory impairment. Close collaboration with dermatologists is essential for treating delusional parasitosis and dysmorphophobia to ensure timely referrals to psychiatry. CONCLUSIONS: This review offers many other examples from the literature of the extent of overlap among medical specialties in the evaluation and effective treatment of DD. Optimal patient care requires close collaboration among specialties.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".