Does Depression in Older Medical Inpatients Predict Mortality?
Bibliographic record
Abstract
BACKGROUND: Previous studies of the effect of depression on mortality among older medical inpatients have yielded inconsistent results. We examined the effects on mortality of both a diagnosis of depression at hospital admission and a history of previous depression, taking into account potential sources of bias (sample selection and confounding). METHODS: Medical inpatients aged 65+ with at most mild cognitive impairment were recruited at two Montreal hospitals and were screened for depression. All those with a diagnosis of major or minor depression (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition [DSM-IV] criteria) and a random sample of nondepressed patients were invited to participate. Baseline data included: history of previous depression, severity of physical illness, comorbidity, and health services utilization. Cox proportional hazards methods were used to analyze survival during the 16- to 52-month follow-up period. RESULTS: Five hundred patients were enrolled; 116 (23.2%) had a history of previous depression. After adjustment for demographic factors, physical illness, cognitive impairment, and prior service utilization, the only depression group with significantly different mortality was patients with both current major depression and a history of depression, who had lower mortality than all other patient groups (hazard ratio 0.42; 95% confidence interval: 0.25, 0.70). CONCLUSIONS: Among patients with no history of depression, a diagnosis of depression was not associated with mortality after adjustment for confounding by physical illness and other factors. Coincident major depression and history of depression was associated with decreased mortality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".