The prevalence of autoimmune disease in patients with esophageal achalasia
Bibliographic record
Abstract
Achalasia is a rare disease of the esophagus that has an unknown etiology. Genetic, infectious, and autoimmune mechanisms have each been proposed. Autoimmune diseases often occur in association with one another, either within a single individual or in a family. There have been separate case reports of patients with both achalasia and one or more autoimmune diseases, but no study has yet determined the prevalence of autoimmune diseases in the achalasia population. This paper aims to compare the prevalence of autoimmune disease in patients with esophageal achalasia to the general population. We retrospectively reviewed the charts of 193 achalasia patients who received treatment at Toronto's University Health Network between January 2000 and May 2010 to identify other autoimmune diseases and a number of control conditions. We determined the general population prevalence of autoimmune diseases from published epidemiological studies. The achalasia sample was, on average, 10-15 years older and had slightly more men than the control populations. Compared to the general population, patients with achalasia were 5.4 times more likely to have type I diabetes mellitus (95% confidence interval [CI] 1.5-19), 8.5 times as likely to have hypothyroidism (95% CI 5.0-14), 37 times as likely to have Sjögren's syndrome (95% CI 1.9-205), 43 times as likely to have systemic lupus erythematosus (95% CI 12-154), and 259 times as likely to have uveitis (95% CI 13-1438). Overall, patients with achalasia were 3.6 times more likely to suffer from any autoimmune condition (95% CI 2.5-5.3). Our findings are consistent with the impression that achalasia's etiology has an autoimmune component. Further research is needed to more conclusively define achalasia as an autoimmune disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".