Forty-Five-Year Mortality Rate as a Function of the Number and Type of Psychiatric Diagnoses Found in a Large Danish Birth Cohort
Bibliographic record
Abstract
OBJECTIVE: Psychiatric comorbidities are common among psychiatric patients and typically associated with poorer clinical prognoses. Subjects of a large Danish birth cohort were used to study the relation between mortality and co-occurring psychiatric diagnoses. METHOD: We searched the Danish Central Psychiatric Research Registry for 8109 birth cohort members aged 45 years. Lifetime psychiatric diagnoses (International Classification of Diseases, Revision 10, group F codes, Mental and Behavioural Disorders, and one Z code) for identified subjects were organized into 14 mutually exclusive diagnostic categories. Mortality rates were examined as a function of number and type of co-occurring diagnoses. RESULTS: Psychiatric outcomes for 1247 subjects were associated with 157 deaths. Early mortality risk in psychiatric patients correlated with the number of diagnostic categories (Wald χ² = 25.0, df = 1, P < 0.001). This global relation was true for anxiety and personality disorders, but not for schizophrenia and substance abuse, which had intrinsically high mortality rates with no comorbidities. CONCLUSIONS: Risk of early mortality among psychiatric patients appears to be a function of both the number and the type of psychiatric diagnoses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".