Making Sense of Variations in Prevalence Estimates of Depression in Cancer: A Co-Calibration of Commonly Used Depression Scales Using Rasch Analysis
Bibliographic record
Abstract
BACKGROUND: The use of different depression self-report scales warrants co-calibration studies to establish relationships between scores from 2 or more scales. The goal of this study was to examine variations in measurement across 5 commonly used scales to measure depression among patients with cancer: Hospital Anxiety and Depression Scale-Depression subscale (HADS-D), Centre for Epidemiologic Studies Depression Scale (CES-D), Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory-II (BDI-II), and Depression Anxiety and Stress Scale-Depression subscale (DASS-D). METHODS: The depression scales were completed by 162 patients with cancer. Participants were also assessed by the major depressive episode module of the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th Edition. Rasch analysis and receiver operating characteristic curves were performed. RESULTS: Rasch analysis of the 5 scales indicated that these all measured depression. The HADS and BDI-II had the widest measurement range, whereas the DASS-D had the narrowest range. Co-calibration revealed that the cutoff scores across the scales were not equivalent. The mild cutoff score on the PHQ-9 was easier to meet than the mild cutoff score on the CES-D, BDI-II, and DASS-D. The HADS-D possible cutoff score was equivalent to cutoff scores for major to severe depression on the other scales. Optimal cutoff scores for clinical assessment of depression were in the mild to moderate depression range for most scales. CONCLUSIONS: The labels of depression associated with the different scales are not equivalent. Most markedly, the HADS-D possible case cutoff score represents a much higher level of depression than equivalent scores on other scales. Therefore, use of different scales will lead to different estimates of prevalence of depression when used in the same sample.
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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 | 0.078 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".