Low-dose Glucocorticoid Use Does Not Reduce Biologic Use in Early RA, Nor Do Biologics Reduce the Need for Glucocorticoids
Bibliographic record
Abstract
The persistent use of glucocorticoids (GC) has been associated with higher risks of osteoporosis, fractures, infection, and cardiovascular events. Although more costly, biologic disease-modifying antirheumatic drug (bDMARD) use could limit complications from prolonged exposures to GC in those patients at risk. The question arises as to whether this is occurring in settings of usual care. Patterns of bDMARD and GC use have rarely been studied in large populations, and factors that predict the initiation or persistent use of either or both are relevant to patients, providers, and payers. In this edition of The Journal , George, et al 1 examined predictors associated with time to first bDMARD use from the time methotrexate (MTX) was first initiated (index date). GC users were those who had started GC within 90 days of the index date, and used GC consistently over the 2-year study window. The study’s 17,415-person rheumatoid arthritis (RA) cohort of incident MTX users, classified as RA using diagnostic codes, was treated between 2005 and 2016. Patients were identified from 3 linked US Veterans Affairs (VA) national administrative databases, and recorded data were used from real-world settings for 2 years from the index date. Initiation … Address correspondence to Dr. V.P. Bykerk, Hospital for Special Surgery, 535 East 70th St., New York, New York 10021, USA. E-mail: bykerkv{at}hss.edu
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".