Risk Factors for Depression Among Elderly Community Subjects: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to determine risk factors for depression among elderly community subjects. METHOD: MEDLINE and PsycINFO were searched for potentially relevant articles published from January 1966 to June 2001 and from January 1967 to June 2001, respectively. The bibliographies of relevant articles were searched for additional references. Twenty studies met the following six inclusion criteria: original research reported in an English or French publication, study group of community residents, age of subjects 50 years or more, prospective study design, examination of at least one risk factor, and use of an acceptable definition of depression. The validity of studies was assessed according to the four primary criteria for risk factor studies described by the Evidence-Based Medicine Working Group. Information about group size at baseline and follow-up, age, proportion of men, depression criteria, exclusion criteria at baseline, length of follow-up, number of incident cases of depression, and risk factors was abstracted from each report. RESULTS: Follow-up of the inception cohort was incomplete in most studies. In the qualitative meta-analysis, risk factors identified by both univariate and multivariate techniques in at least two studies each were disability, new medical illness, poor health status, prior depression, poor self-perceived health, and bereavement. In the quantitative meta-analysis, bereavement, sleep disturbance, disability, prior depression, and female gender were significant risk factors. CONCLUSIONS: Despite the methodologic limitations of the studies and this meta-analysis, bereavement, sleep disturbance, disability, prior depression, and female gender appear to be important risk factors for depression among elderly community subjects.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".