Mortality Among Patients Admitted in a Psychiatric Facility: A Single-Centre Review
Bibliographic record
Abstract
Background: There is higher global mortality among persons living with mental illness than the general population, attributed to the risky behaviours associated with mental illness, medical comorbidities, or side effects of psychiatric medications that result in premature death among psychiatric in patients. Objective: This audit aimed to describe the characteristics of patients with mental illness who died during admission at a tertiary psychiatric ward in Uganda. Methods: This was a retrospective chart review of patients who died while admitted to a psychiatry ward between January 1995 to July 2020. Results: We reviewed 30 charts, of which 18 (60%) patients were women. The majority of patients died during 2002 (13.3%). Many were diagnosed with a brief psychotic disorder, 7 (23.3%), followed by bipolar disorder 6 (20.0%). HIV and epilepsy were the most common comorbidities. The majority of the death causes of death were unknown, 20 (66.7), but heart attack (n=2) was the most identified cause. Conclusion: The majority of the causes of mortality were unknown. The most common cause of mortality was heart attack, a common effect of metabolic disease and chronic antipsychotic use. Mortality audits are warranted to identify possible causes and to develop strategic interventions for mortality prevention. Keywords: mortality, audit, psychiatric inpatients, suicide, antipsychotic medications, medical comorbidities, HIV/AIDS, hospitalization, mental health, epilepsy, continued care
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".