A bifactor model of the Beck Depression Inventory and its association with medical prognosis after myocardial infarction.
Bibliographic record
Abstract
OBJECTIVES: Evidence suggests that depression is associated with adverse outcomes in patients with myocardial infarction (MI). Some of the symptoms of depression may also be symptoms of somatic illness and these may confound the association between depression and prognosis. We investigated whether depression following MI is associated with medical prognosis independent of these somatic symptoms. METHOD: The database of an individual patient data meta-analysis was used. Endpoints were all-cause mortality and cardiovascular events. Nine studies were included. Bifactor factor analysis included 13,100 participants and 7,595 participants were included in survival models. Dimensions were generated from the Beck Depression Inventory using factor analyses. The prognostic association was assessed using mixed-effects Cox regression analysis. RESULTS: A bifactor model, consisting of a general factor and 2 general depression-free subgroup factors (a somatic/affective and a cognitive/affective), provided the best fit. There was a significant association between the general depression factor and all-cause mortality (hazard ratio [HR] = 1.25; 95% confidence interval [CI] [1.17, 1.34], p < .001) and cardiovascular events (HR = 1.18; 95% CI [1.13, 1.23], p < .001). After adjustment for demographics, measures of cardiac disease severity, and health-related variables, the association between the general depression factor and all-cause mortality (HR = 1.14; 95% CI [1.04, 1.25], p = .003) and cardiovascular events (HR = 1.16; 95% CI [1.10, 1.23], p = .014) attenuated. Additionally, the general depression-free somatic/affective factor was significantly associated with the endpoints, while the general depression-free cognitive/affective was not. CONCLUSIONS: A general depression factor is associated with adverse medical prognosis following MI independent of somatic/affective symptoms that may be partly attributable to somatic illness. (PsycINFO Database Record
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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 | 0.036 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".