Comparing the use of the Taiwanese Depression Questionnaire and Beck Depression Inventory for screening depression in patients with chronic pain.
Bibliographic record
Abstract
BACKGROUND: Studies have shown that the validity of self-reported depression questionnaires may be influenced by somatic symptoms such as chronic pain. The purpose of this study was to compare the validity of two self-reported questionnaires, the Taiwanese Depression Questionnaire (TDQ) and the Beck Depression Inventory (BDI), for screening depression in patients with chronic pain. METHODS: One hundred patients with chronic pain were enrolled and assessed using the TDQ, BDI, McGill Pain Questionnaire, and Structured Clinical Interview for DSM-III-R. Seventy-three of them were diagnosed with depressive disorders. Conventional validity indices of the TDQ and BDI were examined and compared. RESULTS: Both the TDQ and BDI had satisfactory sensitivity, specificity, positive predictive value, and negative predictive value. Our results showed a trend that the validity of the TDQ was better than that of the BDI, and the validity of the cognitive/affective components of the TDQ was significantly better than that of the BDI. CONCLUSION: Our results suggest that the TDQ is superior to the BDI in detecting depression in patients with chronic pain in Taiwan.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".