Care Gap in Patients with Early Inflammatory Arthritis with a High Fracture Risk Identified Using FRAX<sup>®</sup>
Bibliographic record
Abstract
OBJECTIVE: To determine the proportion of patients with early inflammatory arthritis in a Canadian cohort who are at high risk for a major osteoporotic fracture using the Fracture Risk Assessment Tool (FRAX(®)), and to determine if a care gap exists in high-risk patients. METHODS: FRAX was applied to 238 patients enrolled in the Canadian Early Arthritis Cohort (CATCH) study based on norms from the United States and the United Kingdom, without the use of bone mineral density measurements. RESULTS: FRAX identified 5%-13% of patients at high risk for fracture, using a conservative analysis. Based on US norms, there was a significant correlation between increasing fracture risk groups and oral glucocorticoid use (p = 0.012) and baseline erosions (p = 0.040). Calcium or vitamin D use did not vary among the different fracture risk groups (p = NS), nor did bisphosphonate use (p = NS). The Disease Activity Score with 28 joint count in the high-risk group was significantly higher compared to the low-risk group (p = 0.048). CONCLUSION: Patients at increased risk had higher disease activity, more frequent glucocorticoid use, and more baseline erosions compared to patients at low risk. A care gap exists, in that a very low proportion of patients at high risk are being treated with calcium, vitamin D, and/or bisphosphonates. A higher fracture risk was calculated in our cohort using the US FRAX calculation tool compared to the UK calculation tool. These data highlight the need to identify and modify fracture risk in patients with early inflammatory arthritis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".