Beck depression inventory-II: Determination and comparison of its diagnostic accuracy in asthmatic outpatients
Bibliographic record
Abstract
Objectives: The Beck Depression Inventory-II (BDI-II) is widely used to assess severity of depressive symptoms and screen for major depressive disorder (MDD) in asthmatic patients. No studies have yet determined its performance accuracy while adjusting for potential confounders. This study aimed to evaluate the impact of covariates on performance accuracy of the BDI-II, and determine the optimal cut-off score for the BDI-II in asthmatic patients. Differences of optimal cut-off scores were also verified across covariate subgroups. Methods: A sample of 668 adult asthmatic outpatients completed the BDI-II and the PRIME-MD – a psychiatric interview used as the reference standard for determining diagnosis of MDD. A method by Janes and Pepe was used to adjust the receiver operating characteristics (ROC) curve of the BDI-II for sex, level of education, smoking status, obesity, age, anxiety disorder, and psychotropic medication. The ROC analyses were conducted to determine optimal cut-off scores. Results: From the total sample, 84(13%) patients met criteria for MDD according to the PRIME-MD. After adjusted for covariates, the area under the ROC curve was significantly smaller than the unadjusted curve [0.88(95%CI, 0.83 to 0.93) vs 0.92(0.89 to 0.95); ΔAUC = –.04(–.06 to –.01)]. While the optimal cut-off score was 13 for the total sample (sens=86%, specif=84%), the analyses indicated different cut-off scores across covariate subgroups, e.g., sex (women,14; men,13), smoking status (current,16; never/ever,13) and obesity (yes,14; no,12). Conclusion: The present study suggests that covariates can affect the classification accuracy of the BDI-II’s original recommended cut-off score.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".