The impact of the COVID-19 pandemic on health outcomes in delusional disorder: A systematic review
Bibliographic record
Abstract
Introduction The health impact of the COVID-19 pandemic has been widely recognized in both physical and mental health. Relatively little attention has been paid to patients with delusional disorder (DD). Objectives Our goal was to synthesize the known mental and physical health consequences of the COVID-19 pandemic in patients diagnosed with DD. Methods A systematic review was carried out using the PubMed and Scopus database (2019-October 2022) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Search terms: “delusional disorder” or “delusional disorder” AND “COVID-19 OR SARS-CoV2.” Inclusion criteria: 1)DD according to DSM/ICD, 2)languages: English, French, German and Spanish, 3)studies reporting health consequences of COVID-19 pandemic. From a total of 615 records, 6 were included: meta-analysis (n=1), cross-sectional studies (n=2), retrospective study (n=1), case reports (n=2). Results A full third of patients with psychosis (including DD) presented with increased psychiatric symptom severity, reportedly activated by increased daily life stress. Suicidal behavior was reported in a previously undiagnosed DD patient in association with a worsening clinical picture. Perhaps surprisingly, admissions for DD in 2020 were lower than in 2019. The duration of hospitalization was, however, longer. There was a report of new onset DD with delusional material centred on COVID. There was also a report of COVID-19 symptoms being more severe in DD patients than in the larger community. Conclusions Health emergencies affect the seriously mentally ill more than other community members. Awareness and outreach can help to maintain treatment adherence and minimize risk of psychotic exacerbation. 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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".