Systemic sclerosis patients are at higher risk of hyperthyroidism and have a worse survival than those without hyperthyroidism: A nationwide population‐based cohort study
Bibliographic record
Abstract
BACKGROUND: A high prevalence of thyroid disorders has been reported in patients with autoimmune diseases. The link between hyperthyroidism and systemic sclerosis (SSc) has been relatively overlooked, and only a few studies utilizing small samples or case reports have been reported so far. OBJECTIVES: To investigate the association between SSc and hyperthyroidism. METHODS: We designed a case-control study utilizing the medical database of the Clalit Health Services. Chi-square and t tests were used for univariate analysis, and a logistic regression model was used for multivariate analysis. RESULTS: The study included 2,431 SSc patients and 12,710 age- and sex-matched controls. The mean age of the study population was 63.32 ± 18.06 years (median 66 years), and female-to-male ratio was 4.5:1. Age (P < .0001, OR 1.03 [95% CI 1.02-1.04]), female sex (P = .0015, OR 1.86 [95% CI 1.27- 2.74]) and diagnosis of SSc (P = .0011, OR 1.81[95% CI 1.27-2.58]) were all independently associated with hyperthyroidism. Patients with SSc and hyperthyroidism had 1.54-fold increase of mortality rates during a mean follow-up of 17 years than SSc patients without hyperthyroidism, even though at the Cox multivariate survival analysis, only age (HR 1.06 [95% CI 1.06-1.07], P < .0001) and diagnosis of SSc (HR 2.35 [CI 2.06 to 2.69], P < .0001) resulted associated with a higher risk of mortality. CONCLUSIONS: Hyperthyroidism is highly prevalent among SSc patients and can negatively impact on their survival rates. Therefore, a pre-emptive screening may be warranted in all SSc patients. Further studies are needed to evaluate whether tight control and optimal treatment for hyperthyroidism may lead to a reduction of all-cause mortality in patients with SSc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".