Detecting recurrent major depressive disorder within primary care rapidly and reliably using short questionnaire measures
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) is often a chronic disorder with relapses usually detected and managed in primary care using a validated depression symptom questionnaire. However, for individuals with recurrent depression the choice of which questionnaire to use and whether a shorter measure could suffice is not established. AIM: To compare the nine-item Patient Health Questionnaire (PHQ-9), the Beck Depression Inventory, and the Hospital Anxiety and Depression Scale against shorter PHQ-derived measures for detecting episodes of DSM-IV major depression in primary care patients with recurrent MDD. DESIGN AND SETTING: Diagnostic accuracy study of adults with recurrent depression in primary care predominantly from Wales METHOD: Scores on each of the depression questionnaire measures were compared with the results of a semi-structured clinical diagnostic interview using Receiver Operating Characteristic curve analysis for 337 adults with recurrent MDD. RESULTS: Concurrent questionnaire and interview data were available for 272 participants. The one-month prevalence rate of depression was 22.2%. The area under the curve (AUC) and positive predictive value (PPV) at the derived optimal cut-off value for the three longer questionnaires were comparable (AUC = 0.86-0.90, PPV = 49.4-58.4%) but the AUC for the PHQ-9 was significantly greater than for the PHQ-2. However, by supplementing the PHQ-2 score with items on problems concentrating and feeling slowed down or restless, the AUC (0.91) and the PPV (55.3%) were comparable with those for the PHQ-9. CONCLUSION: A novel four-item PHQ-based questionnaire measure of depression performs equivalently to three longer depression questionnaires in identifying depression relapse in patients with recurrent MDD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".