Where and How People With Schizophrenia Die
Bibliographic record
Abstract
Article AbstractObjective: To compare place and cause of death for people with and without schizophrenia in Manitoba, Canada. Method: By using deidentified administrative databases at the Manitoba Centre for Health Policy, a 1:3 matched cohort of decedents aged ≥ 10 years in fiscal years April 1995-March 2008 (n = 3,943 with schizophrenia; n = 11,827 without schizophrenia) was selected and matched on age, sex, geography, and date of death ± 2 months. Schizophrenia was defined as ICD-9-CM code 295 or ICD-10-CA codes F20, F21, F23.2, or F25 in hospital/physician files at least once within 12 years of death. Results: The median age at death was 77 years. The attributable percentage of deaths was higher for respiratory illnesses (all ages) and suicide (age 10-59 years only), similar for circulatory illnesses, and lower for cancer in decedents with schizophrenia compared to matched controls. For cancer deaths, decedents with schizophrenia were equally likely to die of gastrointestinal, breast, or prostate cancer, but more likely to die of lung cancer at ages 10-59 (32.5% versus 20.6%, P < .004). Place of death was more likely a nursing home (29.7% vs 13.9%) and less likely a hospital (55.5% vs 70.5%) (P < .0001) for decedents with schizophrenia overall and by specific cause, with the exception of suicide deaths showing no difference by place. Except for those who died in nursing homes, decedents with schizophrenia had higher general practitioner but lower specialist rates and inpatient hospital separations. Conclusions: Generally, patients with schizophrenia were more likely to die in nursing homes but less likely to die in hospitals. Understanding where these patients die is critical for improving access to quality palliative end-of-life care. J Clin Psychiatry 2013;74(6):e551-e557 © Copyright 2013 Physicians Postgraduate Press, Inc. Submitted: October 15, 2012; accepted February 25, 2013 (doi:10.4088/JCP.12m08234). Corresponding author: Patricia J. Martens PhD, Manitoba Centre for Health Policy, 408 - 727 McDermot Ave, Winnipeg, MB, Canada R3E 3P5 (Pat_Martens@cpe.umanitoba.ca).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".