Gastro-oesophageal reflux disease in children with neurological impairment: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: To determine the incidence and prevalence of gastro-oesophageal reflux disease (GERD) diagnosis and treatment in children with neurological impairment (NI) along with relationship to key variables. DESIGN: This is a population-based retrospective cohort study. SETTING: This study takes place in Alberta, Canada. PATIENTS: Children with NI were identified by hospital-based International Classification of Diseases (ICD) codes from 2006 to 2018. MAIN OUTCOME MEASURES: Incidence and prevalence of a GERD diagnosis identified by: (1) hospital-based ICD-10 codes; (2) specialist claims; (3) dispensation of acid-suppressing medication (ASM). Age, gender, complex chronic conditions (CCC) and technology assistance were covariates. RESULTS: Among 10 309 children with NI, 2772 (26.9%) met the GERD definition. The unadjusted incidence rate was 52.1 per 1000 person-years (50.2-54.1). Increasing numbers of CCCs were associated with a higher risk of GERD. The HR for GERD associated with a gastrostomy tube was 4.56 (95% CI 4.15 to 5.00). Overall, 2486 (24.1%) of the children were treated with ASMs of which 1535 (61.7%) met no other GERD criteria. The incidence rate was 16.9 dispensations per year (95% CI 16.73 to 17.07). The prevalence of gastrojejunostomy tubes was 1.1% (n=121), surgical jejunostomy tubes was 0.7% (n=79) and fundoplication was 3.4% (n=351). CONCLUSIONS: The incidence of GERD in children with NI greatly exceeds that of the general paediatric population. Similarly, incidence rate of medication dispensations was closer to the rates seen in adults particularly in children with multiple CCCs and gastrostomy tubes. Further research is needed to determine the appropriate use of ASMs balancing the potential for adverse effects in this population.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".