Relationship Between Disease Characteristics and Oral Radiologic Findings in Systemic Sclerosis: Results From a Canadian Oral Health Study
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc; scleroderma) is associated with a wide periodontal ligament (PDL) and mandibular erosions. We investigated the clinical correlates of SSc with these radiologic abnormalities. METHODS: Subjects from the Canadian Scleroderma Research Group cohort underwent detailed radiologic examinations. Associations between radiologic abnormalities and clinical manifestations of SSc were examined with univariate and multivariate analyses. RESULTS: The study included 159 subjects; 90.6% were women, the mean ± SD age was 56 ± 10 years, diffuse disease was present in 28.3%, and mean ± SD disease duration was 13.7 ± 8.4 years. Widening of the PDL involving at least 1 tooth was present in 38% of subjects, and 14.5% had at least 1 site in the mandible with an erosion. In analyses adjusting for age, disease duration, sex, smoking, and education, we found significant associations between the number of teeth with widening of the PDL and disease severity assessed by the physician global assessment (PGA) (relative risk [RR] 1.19, 95% confidence interval [95% CI] 1.02-1.39, P = 0.028). Analyses replacing the PGA with the skin score, disease subset, or anti-topoisomerase I antibodies confirmed the relationship with indices of disease severity. There was no relationship between either the number of teeth with periodontal disease or the number of missing teeth, and the number of teeth with wide PDL. A smaller interdental distance (RR 0.89, 95% CI 0.82-0.97, P = 0.006), but not disease severity, facial skin score, or ischemia was associated with a larger number of erosions. CONCLUSION: In SSc, a wide PDL may reflect generalized overproduction of collagen, and mandibular erosions are related to local factors in the oral cavity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".