Does a single-item measure of depression predict mortality?
Bibliographic record
Abstract
OBJECTIVE: To determine if a single-item measure of depression predicts mortality over 5 years. DESIGN: Secondary analysis of a population-based cohort study. SETTING: Province of Manitoba. PARTICIPANTS: A total of 1751 community-dwelling adults aged 65 years or older. MAIN OUTCOME MEASURES: Self-reported depression; age, sex, education, functional status, and cognition; death over 5 years. Depression was measured with 1 item drawn from the Center for Epidemiologic Studies Depression (CES-D) scale: "I felt depressed." Bivariate and multivariate analyses were conducted. RESULTS: Those with self-reported depression had a 5-year mortality of 30.2% versus 19.7% in those without self-reported depression (P < .001, chi2). This association persisted after adjustment for age, sex, education, functional status, and cognition: adjusted odds ratio for mortality 1.35 (95% confidence interval 1.03 to 1.76). Among those with cognitive impairment, however, neither the CES-D scale nor the single-item measure predicted mortality. CONCLUSION: A simple measure of depression drawn from the CES-D predicts mortality among cognitively intact community-dwelling older adults, but not among cognitively impaired older adults. Further study is needed in order to determine the usefulness of this question in clinical practice.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".