Postdischarge Mortality in a Cohort Hospitalized With Anorexia Nervosa
Bibliographic record
Abstract
OBJECTIVE: To characterize mortality after hospital discharge in cohorts with and without anorexia nervosa (AN). METHODS: We obtained data for all hospitalizations for psychiatric reasons in Canada (except Quebec) between April 1, 2006, and March 31, 2021 (n = 1.3 million admissions). Cases of AN were identified using ICD-10 (F50.0 and F50.1) codes. First admissions during this interval for AN and other psychiatric conditions were linked to vital statistics data. Mortality was characterized through cross-tabulation, Cox proportional hazards models, and competing cause regression. RESULTS: After adjustment for age and sex, there was no significant difference in mortality between AN and those with other psychiatric conditions (HR = 1.04; p = 0.644). Among AN admissions, 25% (95% CI 18.6-31.4) of deaths were attributed to psychiatric conditions (ICD-F codes), with 88% of these (comprising 22% of all deaths in the AN group) having AN itself identified as the underlying cause of death. In contrast, only 8% of deaths among non-AN admissions were attributed to a mental disorder. DISCUSSION: Prevention of premature mortality in the general psychiatric population emphasizes modification of metabolic (e.g., hyperlipidemia) and lifestyle-related (e.g., sedentary behavior) risk factors. However, as AN itself makes a major contribution to mortality, specialized preventive strategies may be required.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".