Beck Depression Inventory II: determination and comparison of its diagnostic accuracy in cardiac outpatients
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of covariates on performance accuracy of the Beck Depression Inventory II (BDI-II) and to determine the optimal cut-off score for the BDI-II in cardiac outpatients. Differences of optimal cut-off scores were also verified across covariate subgroups. DESIGN AND SETTING: Prospective cross-sectional study at the Department of Nuclear Medicine of the Montreal Heart Institute (Quebec, Canada). METHODS: A total of 750 adult cardiac outpatients (mean ± SD age 58 ± 10 years, 31% women) completed the BDI-II and the Primary Care Evaluation of Mental Disorders (PRIME-MD; a psychiatric interview used as the reference standard for determining diagnosis of major depressive disorder). The receiver operating characteristics (ROC) curve of the BDI-II was adjusted for age, sex, level of education, smoking status, obesity, anxiety disorder, psychotropic medication, and history of coronary artery disease. The ROC analyses were conducted to determine optimal cut-off scores. RESULTS: Forty-two (6%) patients met criteria for current major depressive disorder according to the PRIME-MD. After adjusted for covariates, the area under the ROC curve was significantly smaller than the unadjusted curve (0.76, 95% CI 0.66 to 0.85 vs. 0.84, 95% CI 0.77 to 0.89; ΔAUC = -0.07, 95% CI -0.13 to -0.02). While the optimal cut-off score was 10 for the total sample (sensitivity 83%, specificity 73%), the analyses indicated different cut-off scores across covariate subgroups: e.g. sex (women 13; men 10), and anxiety disorders (yes 15; no 10). CONCLUSIONS: BDI-II is a good screening instrument for depression in cardiac outpatients. However, the present results suggest that covariates can affect the classification accuracy of the BDI-II's original recommended cut-off score. Scholars and clinicians should be aware of the principle that a screening score established in one population may not be relevant to another.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".