Demographic and Clinical Factors Associated with Physician Service Use in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To assess physician service use in a large sample of patients with systemic sclerosis (SSc), and to determine factors associated with physician use. METHODS: Our sample was a national SSc registry maintaining data on demographics (age, sex, race/ethnicity, education, income) and clinical factors (disease onset, organ involvement, etc.). Registry cohort members completed detailed questionnaires, and rheumatologists provided clinical assessments. We examined cross-sectional data from 397 patients who provided information on physician visits in the past 12 months. Patients were classified as high physician-users if they reported more than the median number (6) of physician visits in the past year. In multivariate logistic regressions, we assessed the independent effects of race/ethnicity, education, degree of skin involvement, comorbidity, and SF-36 scores on physician use. RESULTS: On average, subjects reported 3.8 visits per year to specialty physicians (SD 4.2) and 3.5 visits per year to family physicians (SD 4.3). Regression models suggested the following factors as independently associated with number of physician visits: high skin scores, greater comorbidity, and low physical component summary scores on the SF-36. CONCLUSION: There is evidence of independent relationships between clinical characteristics and physician use by patients with SSc.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".