Critical Issues in the Management of Agitation, Aggression, and End-of-Life in Delusional Disorder: A Mini-Review
Bibliographic record
Abstract
Background: Compared to other psychotic disorders, there is little information about staging care in delusional disorder (DD). Unlike schizophrenia, this is a disorder that begins in middle age, a time at which chronic medical comorbidities have already begun to impact global functioning. With age, the combination of psychological and somatic conditions leads to new behaviours, e.g., agitation, aggression, and behaviours that require specific preventive and interventive measures. With further age, knowledgeable end-of-life care becomes necessary for this population. Aim: The aim of this article was to review existing evidence on the management of these successive phases. Methods: We conducted a narrative review using PubMed and ClinicalTrials.gov and searched for the following terms: (agitation OR aggressivity OR aggression OR palliative OR “end-of-life”) AND (“delusional disorder”). Results: We found that the literature was sparse. Existing evidence suggests that medical causes are frequently at the root of agitation and aggression. With respect to management, de-escalation strategies are generally preferred over pharmacotherapy. Specific delusional syndromes, e.g., de Clérambault, Othello, Capgras, Fregoli, as well as folie à deux, are associated with aggression. The somatic subtype of DD is the one most often requiring palliative care at the end of life. Conclusions: We conclude that insufficient attention has been given to the care needs of the accelerated aging process in DD.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".