Focal fat lesions at vertebral corners on magnetic resonance imaging predict the development of new syndesmophytes in ankylosing spondylitis
Bibliographic record
Abstract
OBJECTIVE: Focal fat infiltration is frequently visible on magnetic resonance imaging (MRI) of the spine in patients with ankylosing spondylitis (AS) and likely reflects postinflammatory tissue metaplasia. To support the concept of coupling between inflammation and new bone formation, we tested the hypothesis that focal fat infiltration at a vertebral corner is more likely to evolve into a de novo syndesmophyte. METHODS: MRI scans were obtained at baseline and radiographs were obtained at baseline and 2 years in 100 AS patients from 2 cohorts: a clinical trial cohort (n = 38) and an observational cohort (n = 62). In the clinical trial cohort, patients were randomized to receive anti-tumor necrosis factor (anti-TNF) therapy or placebo for 12-24 weeks and then open-label treatment for 2 years. In the observational cohort, patients received either standard therapy (n = 36) or anti-TNF therapy (n = 26) for 2 years. Vertebral corner inflammation and fat infiltration were assessed independently by pairs of readers who were blinded with regard to the radiographic findings. RESULTS: New syndesmophytes developed significantly more frequently in vertebral corners with fat in both the clinical trial (10.2%) and the observational (6.5%) cohort as compared to those without either feature on baseline MRI (3.1% [P = 0.008] and 1.4% [P = 0.0002], respectively). Adjusting for within-patient variations in baseline syndesmophytes/ankylosis, vertebral corners that were fat-positive/inflammation-positive significantly predicted new syndesmophytes, with an odds ratio (OR) of 7.6 (95% confidence interval [95% CI] 1.5-38.5 [P = 0.01]), while a model that included baseline variations in both fat and inflammation showed an OR of 5.8 (95% CI 2.2-15.3 [P < 0.001]) for inflammation and an OR of 1.9 (95% CI 0.9-4.1 [P = 0.1]) for fat. CONCLUSION: Our data lend support to the hypothesis that inflammatory lesions evolve into new bone through a process of tissue metaplasia that includes fat infiltration.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".