Hiatal hernia prevalence and natural history on non-contrast CT in the Multi-Ethnic Study of Atherosclerosis (MESA)
Bibliographic record
Abstract
Objective To determine the prevalence, risk factors and natural history of hiatal hernia (HH) on CT in the general population. Materials and methods The Multi-Ethnic Study of Atherosclerosis (MESA) acquired full-lung CT on 3200 subjects, aged 53–94 years. Three blinded observers independently determined presence/absence and type (I–IV) of HH. Associations between HH and participant characteristics were assessed via unadjusted and multivariable-adjusted relative risk regression. HH natural history was assessed compared with prior MESA CT. Results Excellent interobserver agreement was found for presence (κ=0.86) and type of HH (κ=0.97). Among 316 HH identified (prevalence=9.9%), 223 (71%) were type I and 93 (29%) were type III. HH prevalence increased with age, from 2.4% in 6th decade to 16.6% in 9th decade (unadjusted prevalence ratio (PR)=1.1 (95% CI 1.04 to 1.1)). HH prevalence was greater in women (12.7%) than men (7.0%) (unadjusted PR=1.8 (95% CI 1.5 to 2.3)) and associated with proton pump inhibitor use (p<0.001). In 75 participants with HH with 10-year follow-up, median HH area increased from 9.9 cm 2 to 17.9 cm 2 (p=0.02) with a higher mean body mass index (BMI) in subjects with increasing HH size compared with HH decreasing in size: mean BMI=30.2±6.2 vs 26.8±7.2 (p=0.02). Conclusion HH on non-contrast CT is prevalent in the general population, increasing with age, female gender and BMI. Its association with proton pump inhibitor use confirms a role in gastro-oesophageal reflux disease and HH progression is associated with increased BMI. Trial registration number NCT00005487 .
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".