Evaluating the impact of systematic reporting of patient-reported outcome measures on depression and anxiety levels in people with multiple sclerosis: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: The effect of systematic capture of patient-reported outcome measures (PROMs) on depression and anxiety levels in persons with multiple sclerosis (PwMS) is not known. We evaluated the effects of PROMs on depression and anxiety levels in PwMS in a randomized controlled trial. METHODS: PwMS were randomized on a 1:1 basis to intervention or control arms. The intervention group completed PROMs at baseline, 6 months, and 12 months. The control group completed PROMs at baseline and 12 months. OBJECTIVES: Outcomes were the difference in change in PROM scores at 12 months, primarily the Hospital Anxiety and Depression Scale (HADS). RESULTS: 237 PwMS (mean age 48.3 years; 78.9 % of female sex) were randomized at baseline, with 153 (79 in the intervention arm and 74 in the control arm) completing at least 1 PROM at 12-month follow-up. Improvements in the total HADS score (-1.39, 95 % CI -2.55 to -0.22, p = 0.02) and HADS anxiety sub-score (-1.03, 95 % CI -1.72 to -0.34, p < 0.01) were observed in the intervention group compared to the control group. No differences in other PROMs were detected between the groups. CONCLUSIONS: Use of PROMs can reduce anxiety levels in PwMS. TRIAL REGISTRATION: ClinicalTrials.gov: NCT0497954 (https://www. CLINICALTRIALS: gov/study/NCT04979546).
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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 | 0.035 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".