Interrater Reliability and Measurement Error of the Children’s Depression Rating Scale–Revised in Adolescents
Bibliographic record
Abstract
Objective: The Children's Depression Rating Scale-Revised (CDRS-R) is widely used in clinical research to assess depression in adolescents; however, limited research explores its measurement properties. This study aimed to test the interrater reliability of the CDRS-R and describe the corresponding measurement error. Method: A cross-sectional design was used in the context of a controlled clinical trial. The sample consisted of help-seeking adolescents (N = 55, ages 13-18 years, inclusive) experiencing depressive symptoms. A research analyst administered and coded the CDRS-R to adolescents through a virtual video-based platform with audio and video recordings. A second research analyst independently watched and coded recordings. The lower bound of the 95% CI of the intraclass correlation coefficient with respect to absolute agreement between 2 independent raters was hypothesized to be ≥0.70. Results: The reliability of CDRS-R was calculated as an intraclass correlation coefficient of 0.84 (95% CI 0.71 to 0.91), indicating acceptable reliability. The associated standard error of measurement was 4.67, and the mean difference in scores between raters was 1.13. The limits of agreement were -11.59 to 13.84. Conclusion: The findings provide support for the CDRS-R as a tool with adequate interrater reliability to assess depressive symptoms in adolescents. The measurement error parameters can assist in clinical interpretation of differences in scores when adolescents are assessed by multiple raters. Clinical Trial Registration Information: Effectiveness of an Integrated Care Pathway for Depression: Cluster Randomized Controlled Trial (CARIBOU-2); https://clinicaltrials.gov/study/NCT05142683.
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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 | 0.179 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".