Health Literacy Predicts the Discrepancy Between Patient and Provider Global Assessments of Rheumatoid Arthritis Activity at a Public Urban Rheumatology Clinic
Bibliographic record
Abstract
OBJECTIVE: Numerous studies report that significant discordance exists between patient and provider [physician] measures of rheumatoid arthritis (RA). We examined whether health literacy explains this discordance. METHODS: We recruited English-speaking adult patients with RA for this cross-sectional study. Subjects completed 2 versions of patient global assessments of disease activity (PTGA), using standard terminology from the Multi-Dimensional Health Assessment Questionnaire (MDHAQ) and the 28-joint count Disease Activity Score 28 (DAS28). The provider global assessment (MDGA) was also obtained. The discrepancy between PTGA and MDGA was calculated as the absolute difference between these assessments. We used validated instruments [Short Test of Functional Health Literacy in Adults (S-TOFHLA) and Rapid Estimate of Adult Literacy in Medicine (REALM)] and linear regression to determine whether health literacy predicts disease measure discrepancy. RESULTS: The study included 110 subjects. Limited health literacy was a common finding by both the REALM and S-TOFHLA. PTGA and MDGA showed fair to good correlation (r = 0.66-0.68), although both versions of the PTGA were significantly higher than MDGA by the t-test (p < 0.001). The S-TOFHLA and REALM both were associated with the absolute difference between the MDGA and PTGA by linear regression, and results remained statistically significant in multivariate analysis. CONCLUSION: Health literacy was independently associated with the extent of discrepancy between PTGA and MDGA in English-speaking patients with RA at an urban clinic. This finding should influence our interpretation of disease measures.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".