Association of vesicoureteral reflux and gastroesophageal reflux disease in children: A population-based study
Bibliographic record
Abstract
INTRODUCTION: Practitioners have anecdotally hinted at a possible association between gastroesophageal reflux disease (GERD) and vesicoureteral reflux (VUR). We sought to identify an association in diagnosis between GERD and VUR using a population-based dataset in a well-defined geographic area covered by a single-payer healthcare system. METHODS: A retrospective review of individuals aged 0-16 years registered in the Nova Scotia Medical Service Insurance database from January 1997 to December 2012 was completed. Presence of GERD and VUR were ascertained based on billing codes. The baseline prevalence of GERD and VUR was calculated for this population for the same time period. Proportions of VUR patients with and without GERD were compared. The risk of being diagnosed with VUR in patients with GERD controlling for sex was calculated. RESULTS: Of 404 300 patients identified, 6.6% had a diagnosis of GERD (n=27 092), 0.33% had a diagnosis of VUR (n=1348), and 0.08% were diagnosed with both (n=327). Among patients with VUR, the prevalence of GERD was 24.3% compared to 6.6% in patients without VUR (p<0.0001). Among patients with GERD, the prevalence of VUR was 1.2% compared to 0.27% in patients without (p<0.0001). The risk of being diagnosed with VUR was higher in the presence of GERD (odds ratio [OR] 4.49; 95% confidence interval [CI] 3.96-5.09; p<0.0001), irrespective of sex. CONCLUSIONS: The odds of being diagnosed with VUR is more than 4.5 times higher in an individual with GERD. The clinical significance of this association remains to be explored.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".