Practice Variance, Prevalence, and Economic Burden of Premature Infants Diagnosed With GERD
Bibliographic record
Abstract
OBJECTIVE: To determine the practice variance, prevalence, and economic burden of clinically diagnosed gastroesophageal reflux disease (GERD) in preterm infants. METHODS: Applying a retrospective cohort study design, we analyzed data from 18 567 preterm infants of 22 to 36 weeks' gestation and >400 g birth weight from the NICUs of 33 freestanding children's hospitals in the United States. GERD prevalence, comorbidities, and demographic factors were examined for their association with average length of stay (LOS) and hospitalization cost. RESULTS: Overall, 10.3% of infants received a diagnosis of GERD (95% confidence interval [CI]: 9.8-10.7). There was a 13-fold variation in GERD rates across hospitals (P < .001). GERD diagnosis was significantly (P < .05) associated with bronchopulmonary dysplasia and necrotizing enterocolitis, as well as congenital anomalies and decreased birth weight. GERD diagnosis was associated with $70 489 (95% CI: 62 184-78 794) additional costs per discharge and 29.9 additional days in LOS (95% CI: 27.3-32.5). CONCLUSIONS: One in 10 of these premature NICU infants were diagnosed with GERD, which is associated with substantially increased LOS and elevated costs. Better diagnostic and management strategies are needed to evaluate reflux-type symptoms in this vulnerable NICU 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 | 0.003 | 0.026 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".