Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis
Bibliographic record
Abstract
BACKGROUND: The brief Patient Health Questionnaire (PHQ-9) is commonly used to screen for depression with 10 often recommended as the cut-off score. We summarized the psychometric properties of the PHQ-9 across a range of studies and cut-off scores to select the optimal cut-off for detecting depression. METHODS: We searched Embase, MEDLINE and PsycINFO from 1999 to August 2010 for studies that reported the diagnostic accuracy of PHQ-9 to diagnose major depressive disorders. We calculated summary sensitivity, specificity, likelihood ratios and diagnostic odds ratios for detecting major depressive disorder at different cut-off scores and in different settings. We used random-effects bivariate meta-analysis at cutoff points between 7 and 15 to produce summary receiver operating characteristic curves. RESULTS: We identified 18 validation studies (n = 7180) conducted in various clinical settings. Eleven studies provided details about the diagnostic properties of the questionnaire at more than one cut-off score (including 10), four studies reported a cut-off score of 10, and three studies reported cut-off scores other than 10. The pooled specificity results ranged from 0.73 (95% confidence interval [CI] 0.63-0.82) for a cut-off score of 7 to 0.96 (95% CI 0.94-0.97) for a cut-off score of 15. There was major variability in sensitivity for cut-off scores between 7 and 15. There were no substantial differences in the pooled sensitivity and specificity for a range of cut-off scores (8-11). INTERPRETATION: The PHQ-9 was found to have acceptable diagnostic properties for detecting major depressive disorder for cut-off scores between 8 and 11. Authors of future validation studies should consistently report the outcomes for different cut-off scores.
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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 | 0.040 | 0.064 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.081 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".