Dysthyroidism in dermato/polymyositis patients: A case‐control study
Bibliographic record
Abstract
BACKGROUND: Dermatomyositis (DM) and polymyositis (PM) are two rare autoimmune disorders occasionally described with dysthyroidism; however, no solid evidence still proves such an association. AIM: To evaluate the prevalence of dysthyroidism among DM/PM patients. DESIGN AND SETTING: A nation-wide case-control study was conducted. METHODS: From the Clalit Health Services health records database, we extracted 2085 (DM = 1475 (70.7%), PM = 610 (29.3%)) PM/DM cases and 10 193 sex-age matched controls in the period 2000-2018. Both univariate and multivariate analyses were performed to evaluate the link dysthyroidism and PM/DM. Survival analysis was also performed. RESULTS: The rate of hyperthyroidism was significantly (P = .0097) higher in cases (n = 40, 1.9%) with respect to controls (n = 123, 1.2%). Similarly, the rate of hypothyroidism was significantly (P < .0001) associated with cases (n = 234, 11.2%) when compared to controls (n = 853, 8.4%). At the multivariate logistic regression analysis, both DM (OR 1.31 [95%CI 1.07-1.60], P = .0087) and PM (OR 1.54 [95%CI 1.21-1.95], P = .004) were significantly associated with hypothyroidism, whereas DM (OR 1.70 [95%CI 1.10-2.61], P = .0165) but not PM (OR 1.45 [0.83-2.55], P = .1947) was found to be associated with hyperthyroidism. Subjects with PM and positive for anti-Sjögren's syndrome-related antigen A (SSA) auto-antibody displayed a significant risk of developing hyperthyroidism (OR 5.85 [95%CI 1.02-33.74], P = .0480), whereas individuals with DM and positive for antinuclear antibody (ANA) had a higher risk of developing hyperthyroidism (OR 2.65 [95%CI 1.00-7.03], P = .0498). CONCLUSIONS: Physicians treating PM/DM patients should consider screening for thyroid dysfunction on a regular basis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".