Differences in Longitudinal Disease and Treatment Characteristics of Patients with Rheumatoid Arthritis Replying and Not Replying to a Postal Questionnaire. Experience from a Biologics Register in Southern Sweden
Bibliographic record
Abstract
OBJECTIVE: Studies on patients not answering postal questionnaires are scarce. We assessed the demographics and longitudinal disease and treatment characteristics of patients with rheumatoid arthritis (RA) in a Swedish biologics register who replied and who did not reply to a postal questionnaire. METHODS: In the South Swedish Arthritis Treatment Group register, we have detailed disease severity characteristics at baseline and at followup for rheumatology patients taking biologic drugs. In 2005 a questionnaire on smoking, comorbidities, education, and ethnicity was sent to 1234 RA patients who had started their first biologic drug. RESULTS: In total, 989 subjects (80%) answered the questionnaire. The 245 (20%) who did not answer generally had more severe RA [higher Disease Activity Score, worse Health Assessment Questionnaire score, higher visual analog scale scores for general health and pain at baseline and at followup, and stopped the drug treatment more frequently (72% vs 53%; p=0.0001)]. There were no statistically significant differences in gender and disease duration between those who replied and those who did not reply, but in general the patients who did not reply were younger. CONCLUSION: Patients with RA in a Swedish biologics register not replying to a postal questionnaire had more severe RA and stopped biological drug treatment more frequently. Thus a detailed analysis of prospectively collected data can clarify selection bias introduced by subjects who do not answer a postal questionnaire, which may influence the validity and interpretation of results from postal survey studies.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".