Quality assurance study of the use of preventative therapies in glucocorticoid-induced osteoporosis in early inflammatory arthritis: results from the CATCH cohort
Bibliographic record
Abstract
OBJECTIVE: To characterize steroid use and compliance with glucocorticoid-induced osteoporosis (GIOP) guidelines within a large early inflammatory arthritis cohort. METHODS: Using the Canadian Early Arthritis Cohort (CATCH) database, patients with inflammatory arthritis on glucocorticoids (oral, IA and i.m.) were identified. Consecutive steroid exposure was defined as using glucocorticoids for two consecutive clinic visits (at least 90 days apart). The primary outcome was the proportion of patients receiving calcium, vitamin D and a bisphosphonate among patients treated with consecutive oral glucocorticoids. RESULTS: Six hundred and fifty-five patients were in the CATCH database, where 273 patients were identified as glucocorticoid users, of whom 48% were on oral prednisone, 38% received i.m. or IA and 13% both. The median oral daily dose of prednisone was 5 mg (interquartile range 2.5-10). Consecutive users (CUs, n = 78) compared with non-consecutive users (NUs, n = 532) showed that CUs were older (56 vs 50 years, P = 0.001); females were fewer (63% vs 74%, P = 0.04), but a similar proportion were RF positive (51% in CU vs 56% in NU, P = 0.73). For the primary outcome, rates of prophylaxis for users of consecutive oral steroids were as follows: 53% were treated with calcium, 47% with vitamin D and 25% were on a bisphosphonate. For users of oral prednisone at doses ≥7.5 mg/day, rates of prophylaxis were as follows: 64% were treated with calcium, 57% with vitamin D and 21% were on a bisphosphonate. CONCLUSION: Glucocorticoid therapy is frequently used in early inflammatory arthritis. The use of calcium, vitamin D or a bisphosphonate was low among chronic glucocorticoid users and illustrates the need for more diligence in patients receiving glucocorticoids to prevent GIOP.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".