Predictors of Longterm Changes in Body Mass Index in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Low body mass index (BMI) is a risk factor for poor longterm outcomes in rheumatoid arthritis (RA). The purpose of this study was to identify factors associated with longterm changes in BMI. METHODS: Subjects with RA from the Veterans Affairs (VA) Rheumatoid Arthritis (VARA) Registry (n = 1474) were studied. Information on inflammatory markers, presence of erosions, and smoking status were extracted from the VARA database. BMI was extracted from VA electronic medical records within 14 days of each visit date. VA pharmacy records were queried to identify prescriptions for specific RA therapies within 1 month of the visit date. We used robust generalized estimating equations marginal regression models to calculate independent associations between clinical variables and BMI over time. Similar models determined predictors of change in weight and risk of weight loss over the subsequent study observation period. RESULTS: Increasing age, active smoking, and the presence of erosions at baseline were associated with lower BMI. Weight decreased over time among older adults. Factors associated with greater reductions in BMI over time and a greater risk of weight loss were higher inflammatory markers, smoking, older age, higher BMI, and less subsequent improvement in inflammation. Methotrexate use was associated with a lower risk of weight loss. The use of prednisone or anti-tumor necrosis factor therapies was not associated with change in BMI or the risk of weight loss independent of other factors. CONCLUSION: Greater age, greater inflammatory activity, and active smoking are associated with greater weight loss in RA over time.
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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 | 0.004 |
| 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".