Screening for Adolescent Depression: Comparison of the Kutcher Adolescent Depression Scale with the Beck Depression Inventory
Bibliographic record
Abstract
Self-report instruments commonly used to assess depression in adolescents have limited or unknown reliability and validity in this age group. We describe a new self-report scale, the Kutcher Adolescent Depression Scale (KADS), designed specifically to diagnose and assess the severity of adolescent depression. This report compares the diagnostic validity of the full 16-item instrument, brief versions of it, and the Beck Depression Inventory (BDI) against the criteria for major depressive episode (MDE) from the Mini International Neuropsychiatric Interview (MINI). Some 309 of 1,712 grade 7 to grade 12 students who completed the BDI had scores that exceeded 15. All were invited for further assessment, of whom 161 agreed to assessment by the KADS, the BDI again, and a MINI diagnostic interview for MDE. Receiver operating characteristic (ROC) curve analysis was used to determine which KADS items best identified subjects experiencing an MDE. Further ROC curve analyses established that the overall diagnostic ability of a six-item subscale of the KADS was at least as good as that of the BDI and was better than that of the full-length KADS. Used with a cutoff score of 6, the six-item KADS achieved sensitivity and specificity rates of 92% and 71%, respectively-a combination not achieved by other self-report instruments. The six-item KADS may prove to be an efficient and effective means of ruling out MDE in adolescents.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".