Increased prevalence of scleroderma in southwestern Ontario: a cluster analysis.
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of scleroderma (systemic sclerosis, SSc) in 3 cities, Windsor, Sarnia, and Woodstock, Ontario, within our referral area, which has a referral base population of 1 million. METHODS: To compare the addresses and exposures of referrals with SSc, we performed a case control study using our patients with scleroderma and 2 age and sex matched controls from the same rheumatologist's practice. RESULTS: Sixty-seven of 91 patients with SSc and 87 of 154 controls responded. The mean age of patients with SSc was 53.2 years versus 52.8 years in controls. There was no statistically significant increase in the number of SSc patients from Windsor (population 197,694): 14 patients (15.4%) with SSc versus 18 controls (11.6%) (p < 0.41); or Sarnia (population 72,738): 7 patients (7.7%) with SSc versus 7 controls (4.5%) (p < 0.31). However, there were 9 cases (9.9%) from Woodstock (population 32,086) versus one control (0.64%) (p < 0.0004). The point prevalence of scleroderma was at least 0.71/10,000 in Windsor, 0.96/10,000 in Sarnia, and 2.8/10,000 in Woodstock. There were no significant between-group differences in exposure to industrial toxins or chemicals including vinyl chloride, silica, and benzene, but exposure rates in both groups were low. Occupations and proportion of those who were work disabled were not different. Patients with SSc were not more likely to have smoked cigarettes (p < 0.43); however, they were more likely to drink at least 6 drinks of alcohol per week (p < 0.04) and had more dental fillings (p < 0.05). Patients with SSc knew on average 3.2 others with this disease, and controls knew only 0.25 others with scleroderma (p < 0.00001). Two patients with SSc knew someone with SSc in their workplace versus none of the controls. CONCLUSION: Our a priori expected higher prevalence of scleroderma in Windsor and Sarnia did not reach significance, but the cluster in Woodstock seems statistically validated, and the exact reason for this cluster remains unclear. It is unlikely that all patients with SSc in Woodstock were seen by us, so the prevalence of scleroderma is at least 2.8/10,000, which is a medium to high prevalence compared to other studies. Associations with alcohol and dental fillings require further study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".