Validity of four screening scales for major depression in MS
Bibliographic record
Abstract
BACKGROUND: There is a role for brief assessment instruments in detection and management of major depression in MS. However, candidate scales have rarely been validated against a validated diagnostic interview. In this study, we evaluated the performance of several candidate scales: Patient Health Questionnaire (PHQ)-9, PHQ-2, Center for Epidemiologic Studies Depression rating scale (CES-D), and Hospital Anxiety and Depression Scale (HADS-D) in relation to the Structured Clinical Interview for DSM-IV (SCID). METHODS: The sample was an unselected series of 152 patients attending a multiple sclerosis (MS) clinic. Participants completed the scales during a clinic visit or returned them by mail. The SCID was administered by telephone within two weeks. The diagnosis of major depressive episode, according to the SCID, was used as a reference standard. Receiver-operator curves (ROC) were fitted and indices of measurement accuracy were calculated. RESULTS: All of the scales performed well, each having an area under the ROC > 90%. For example, the PHQ-9 had 95% sensitivity and 88.3% specificity when scored with a cut-point of 11. This cut-point achieved a 56% positive predictive value for major depression. CONCLUSIONS: While all of the scales performed well in terms of their sensitivity and specificity, the availability of the PHQ-9 in the public domain and its brevity may enhance the feasibility of its use.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".