Evaluation of the 9–Item Patient Health Questionnaire (PHQ–9) as an Assessment Instrument for Symptoms of Depression in Patients with Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Patients with multiple sclerosis (MS) have a high prevalence of depression, but there are concerns regarding assessment of possible depression status using rating scales, such as the 9-item Patient Health Questionnaire (PHQ-9). The idea has been proposed that PHQ-9 scores are contaminated by the MS symptoms of fatigue and impaired concentration, decreasing the validity of measurement. OBJECTIVES: To determine the extent to which scores on the PHQ-9 are contaminated by patients reporting symptoms attributable to MS. METHODS: Baseline PHQ-9 scores from an ongoing prospective cohort study of depression in patients with MS (N = 173) were compared with those of a general population sample (N = 3304). Depression prevalence estimates for the MS and general population samples were calculated using conventional algorithm and cutoff point scoring methods, as well as modified scoring methods, excluding fatigue and concentration deficits. Correlations between scores on adjusted scoring methods were analyzed. The proportion that each item contributed to total PHQ-9 scores was also calculated. A logistic regression model evaluated the relationship between symptom severity and MS status corrected for age, sex, and other depressive symptoms. RESULTS: Conventional PHQ-9 algorithm and cutoff point scoring yielded 2-week prevalence estimates of 9.8% and 21.4%, respectively, in patients with MS, and 3.3% and 8.4%, respectively, in the general population. In both samples, conventional and modified scoring methods were strongly correlated (Spearman rank correlation coefficient > 0.9). The proportion of total scores contributed by fatigue and concentration items was not different between samples. With adjustment for other depressive symptoms, the MS sample had greater odds of endorsement for guilt (odds ratio, 2.17; P = 0.025) and fatigue (odds ratio, 1.51; P = 0.046). CONCLUSION: Inclusion or exclusion of fatigue and concentration items on the PHQ-9 scale does not substantially alter the performance of the scale. With use of the PHQ-9 in MS populations, we find no evidence to suggest that modified approaches to scoring are necessary.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".