Online Patient-Reported Outcome Measure Engagement Is Dependent on Demographics and Locality: Findings From an Observational Cohort
Bibliographic record
Abstract
OBJECTIVE: Online patient-reported outcome measures (PROMs) enable remote collection of perceptions of health status, function, and well-being. We aimed to explore patterns of PROM completion in patients with early inflammatory arthritis (EIA) recruited to the National Early Inflammatory Arthritis Audit (NEIAA). METHODS: NEIAA is an observational cohort study design; we included adults from this cohort with a new diagnosis of EIA from May 2018 to March 2020. The primary outcome was PROM completion at baseline, 3 months, and 12 months. Mixed effects logistic regression and spatial regression models were used to identify associations between demographics (age, gender, ethnicity, deprivation, smoking, and comorbidity), clinical commissioning groups, and PROM completion. RESULTS: Eleven thousand nine hundred eighty-six patients with EIA were included, of whom 5331 (44.5%) completed at least 1 PROM. Patients from ethnic minority backgrounds were less likely to return a PROM (adjusted odds ratio [aOR] 0.57, 95% CI 0.48-0.66). Greater deprivation (aOR 0.73, 95% CI 0.64-0.83), male gender (aOR 0.86, 95% CI 0.78-0.94), higher comorbidity burden (aOR 0.95, 95% CI 0.91-0.99), and current smoker status (aOR 0.73, 95% CI 0.64-0.82) also reduced odds of PROM completion. Spatial analysis identified 2 regions with high (North of England) and low (Southeast of England) PROM completion. CONCLUSION: We define key patient characteristics (including ethnicity) that influence PROM engagement using a national clinical audit. We observed an association between locality and PROM completion, with varying response rates across regions of England. Completion rates could benefit from targeted education for these groups.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".