Achalasia: incidence, prevalence and survival. A population‐based study
Bibliographic record
Abstract
BACKGROUND: Studies of achalasia epidemiology are important as they often yield new insights into disease etiology. In this study, our objective was to carry out the first North American population-based study of achalasia epidemiology using a governmental administrative database. METHODS: All residents in the province of Alberta, Canada receive universal healthcare coverage as a benefit. The provincial health ministry, Alberta Health and Wellness, maintains a central stakeholder database of patient demographic information and physician billing claims. We defined an achalasia case as a billing claim submitted for the years 1996-2007 with an ICD-9-CM code of 530.0 or 530 and a Canadian Classification of Procedure treatment code of 54.92A (endoscopic balloon dilation) or 54.6 (esophagomyotomy). A preliminary validation study of the case definition demonstrated a sensitivity of 85% and specificity of 99% for known cases and controls. KEY RESULTS: A total of 463 achalasia cases were identified from 1995 to 2008 (59.6% males). Mean age at diagnosis was 53.1 years. In 2007, the achalasia incidence was 1.63/100,000 (95% CI 1.20, 2.06) and the prevalence was 10.82/100,000 (95% CI 9.70, 11.93). We observed a steady increase in the overall prevalence rate from 2.51/100,000 in 1996 to 10.82/100,000 in 2007. Survival of achalasia cases was significantly less than age-sex matched population controls (P < 0.0001). CONCLUSIONS & INFERENCES: Using a population-based approach, the incidence and prevalence of treated achalasia is 1.63/100,000 and 10.82/100,000, respectively. The disease appears to have a stable incidence but a rising prevalence. Survival of achalasia cases is significantly less than age-matched healthy controls.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".