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 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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".