High Prevalence of Foot Insufficiency Fractures in Patients With Inflammatory Rheumatic Musculoskeletal Diseases
Bibliographic record
Abstract
Objective To assess the prevalence of foot insufficiency fractures (IF) in patients with rheumatic musculoskeletal disease (RMD) with foot pain. Methods In a retrospective design, 1752 magnetic resonance imaging (MRI) scans of consecutive patients presenting with foot pain in 2 time periods between 2016 and 2018 were evaluated. The group with IF was matched with controls with foot pain without IF. Bone mineral density (BMD) was assessed by dual-energy x-ray absorptiometry. Multivariate analyses were performed. Results A total of 1145 MRI scans of patients (median age 59 yrs, 82.9% female) with an inflammatory (65.4%) and of 607 with no inflammatory (34.6%) RMD (median age 58 yrs, 80.8% female) were available. Most patients had rheumatoid arthritis (RA; 42.2%), and others had psoriatic arthritis (22.4%), axial spondyloarthritis (11.1%), or connective tissue disease (CTD; 7.6%). Foot IF were found in 129 MRI scans of patients (7.5%). There was no difference between time periods. The prevalence of IF was highest in CTD (23%) and RA (11.4%). More patients with an inflammatory than a noninflammatory RMD had IF (9.1% vs 4.1%, respectively; P < 0.001). Using conventional radiography, IF were only detected in 25%. Low BMD and a history of fractures were more frequent in patients with IF than without (42.6% vs 16.2% and 34.9% vs 8.6%, respectively; P < 0.001). Conclusion A high prevalence of foot fractures was found in MRI scans of patients with RMD, many without osteoporosis. MRI was more sensitive than radiographs to detect IF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".