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Record W3171953257 · doi:10.2147/ca.s308757

Mortality Among Patients Admitted in a Psychiatric Facility: A Single-Centre Review

2021· review· en· W3171953257 on OpenAlexaff
Mark Mohan Kaggwa, Sarah Maria Najjuka, Sheila Harms, Scholastic Ashaba

Bibliographic record

VenueClinical Audit · 2021
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychiatryMedicineEmergency medicine

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.856
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.182
GPT teacher head0.454
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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