Association Between Glucocorticoid Exposure and Healthcare Expenditures for Potential Glucocorticoid-related Adverse Events in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Oral glucocorticoid (OGC) use for rheumatoid arthritis (RA) is debated because of the adverse event (AE) profile of OGC. We evaluated the associations between cumulative doses of OGC and potential OGC-related AE, and quantified the associated healthcare expenditures. METHODS: Using the MarketScan databases, patients ≥ 18 years old who have RA with continuous enrollment from January 1 to December 31, 2012 (baseline), and from January 1 to December 31, 2013 (evaluation period), were identified. Cumulative OGC dose was measured using prescription claims during the baseline period. Potential OGC-related AE (osteoporosis, fracture, aseptic necrosis of the bone, type 2 diabetes, ulcer/gastrointestinal bleeding, cataract, hospitalization for opportunistic infection, myocardial infarction, or stroke) and AE-related expenditures (2013 US$) were gathered during the evaluation period. Multivariable regression models were fitted to estimate OR of AE and incremental costs for patients with AE. RESULTS: There were 84,357 patients analyzed, of whom 48% used OGC during the baseline period and 26% had an AE during the evaluation period. A cumulative OGC dose > 1800 mg was associated with an increased risk of any AE compared with no OGC exposure (OR 1.19, 99.65% CI 1.09-1.30). Incremental costs per patient with any AE were significantly greater for cumulative OGC dose > 1800 mg compared with no OGC exposure (incremental cost = $3528, 99.65% CI $2402-$4793). CONCLUSION: Chronic exposure to low to medium doses of OGC was associated with significantly increased risk of potential OGC-related AE in patients with RA, and greater cumulative OGC dose was associated with substantially higher AE-related healthcare expenditures among patients with AE.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".