Delusional Disorder in Old Age: A Hypothesis-Driven Review of Recent Work Focusing on Epidemiology, Clinical Aspects, and Outcomes
Bibliographic record
Abstract
The theme, strength, and duration of a delusion are considered important in distinguishing one psychosis of old age from another. Research results, however, are mostly based on studies conducted on one form of psychosis, namely schizophrenia. The aim of this hypothesis-driven narrative review is to gather clinically important information about the psychosis identified as delusional disorder (DD), as it affects persons of senior age. We hypothesized that DD becomes relatively prevalent in old age, especially in women; and that it is associated with demonstrable brain changes, which, in turn, are associated with cognitive defects and poor pharmacological response, thus increasing the risk of aggression and suicide. Computerized searches in PubMed and ClinicalTrials.gov were conducted using the following search terms: (delusional disorder) AND (elderly OR old OR aged OR psychogeriatrics). A total of 16 recent studies (including case reports) were reviewed. Our hypotheses could not be definitively confirmed because research evidence is lacking. In order to improve eventual outcomes, our literature search demonstrates the need for more targeted, well-designed studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".