The rising incidence of eosinophilic oesophagitis is associated with increasing biopsy rates: a population‐based study
Bibliographic record
Abstract
BACKGROUND: Eosinophilic oesophagitis (EoE) has evolved from a supposedly rare entity to one whose incidence rates are approaching that of inflammatory bowel disease. The factors responsible for this apparent increase in the incidence remain obscure. AIM: To assess various endoscopist and pathologist factors that might affect the frequency of EoE being detected in a well-defined North American population. HYPOTHESIS: Increased endoscopist and pathologist awareness has contributed to the increased clinical recognition of EoE. METHODS: Cases of EoE were identified systematically using population-based pathology and endoscopy databases from January 2004 to December 2008 in Calgary, Canada (population 1.25 million). EoE frequency was estimated with time trend analysis. Characteristics of individual endoscopists (n = 45) were compared with diagnostic rates. RESULTS: Crude population incidence of EoE increased from 2.1 per 10(5) in 2004 to 11.0 per 10(5) in 2008: an annual increase of 39% (P < 0.0001). The frequency in men was 4.5 times higher than in women (95% CI: 3.51-5.76). In patients presenting with dysphagia oesophageal biopsy rates increased from 17.0% in 2004 to 41.3% of EGDs in 2008: an annual rise of 26% (P < 0.0001). On multivariate regression analysis, those endoscopists with higher biopsy rates were more likely to make the diagnosis of EoE (P = 0.008). To include or exclude the diagnosis, typical histological features of EoE were reported more often by pathologists in 2008 as compared to 2004 (P = 0.01 & P < 0.0001 respectively). CONCLUSIONS: The incidence of eosinophilic oesophagitis continues to rise in the general population, in part due to increasing oesophageal biopsy rates and a more detailed histological evaluation of specimens. The biopsy rate of an endoscopist is an indicator for a higher diagnostic yield.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".