Accuracy of Depression Screening Tools to Detect Major Depression in Children and Adolescents: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Depression screening among children and adolescents is controversial, and no clinical trials have evaluated benefits and harms of screening programs. A requirement for effective screening is a screening tool with demonstrated high accuracy. The objective of this systematic review was to evaluate the accuracy of depression screening instruments to detect major depressive disorder (MDD) in children and adolescents. METHOD: Data sources included the MEDLINE, MEDLINE In-Process, EMBASE, PsycINFO, HaPI, and LILACS databases from 2006 to September 30, 2015. Eligible studies compared a depression screening tool to a validated diagnostic interview for MDD and reported accuracy data for children and adolescents aged 6 to 18 years. Risk of bias was assessed with QUADAS-2. RESULTS: We identified 17 studies with data on 20 depression screening tools. Few studies examined the accuracy of the same screening tools. Cut-off scores identified as optimal were inconsistent across studies. Width of 95% confidence intervals (CIs) for sensitivity ranged from 9% to 55% (median 32%), and only 1 study had a lower bound 95% CI ≥80%. For specificity, 95% CI width ranged from 2% to 27% (median 9%), and 3 studies had a lower bound ≥90%. Methodological limitations included small sample sizes, exploratory data analyses to identify optimal cut-offs, and the failure to exclude children and adolescents already diagnosed or treated for depression. CONCLUSIONS: There is insufficient evidence that any depression screening tool and cut-off accurately screens for MDD in children and adolescents. Screening could lead to overdiagnosis and the consumption of scarce health care resources.
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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 | 0.024 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".