Assessment of differential item functioning of the PHQ-9, HADS-D and PROMIS-depression scales in persons with and without multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: We tested for the presence of differential item functioning (DIF) in commonly used measures of depressive symptoms, in people with multiple sclerosis (MS) versus people with a psychiatric disorder without MS. METHODS: Participants included individuals with MS, or with a lifetime history of a depressive or anxiety disorder (Dep/Anx) but no immune-mediated inflammatory disease. Participants completed the Patient Health Questionnaire (PHQ-9), Hospital Anxiety and Depression Scale (HADS), and the Patient Reported Outcome Measurement Information System (PROMIS)-Depression. We assessed unidimensionality of the measures using factor analysis. We evaluated DIF using logistic regression, with and without adjustment for age, gender and body mass index (BMI). RESULTS: We included 555 participants (MS: 252, Dep/Anx: 303). Factor analysis showed that each depression symptom measure had acceptable evidence of unidimensionality. In unadjusted analyses comparing the MS versus Dep/Anx groups we identified multiple items with evidence of DIF, but few items showed DIF effects that were large enough to be clinically meaningful. We observed non-uniform DIF for one PHQ-9 item, and three HADS-D items. We also observed DIF with respect to gender (one HADS-D item), and BMI (one PHQ-9 item). For the MS versus Dep/Anx groups, we no longer observed DIF post-adjustment for age, gender and BMI. On unadjusted and adjusted analyses, we did not observe DIF for any PROMIS-D item. CONCLUSION: Our findings suggest that DIF exists for the PHQ-9 and HADS-D with respect to gender and BMI in clinical samples that include people with MS whereas DIF was not observed for the PROMIS-Depression scale.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 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".