Managing Gastroesophageal Reflux Symptoms in the Very Low-Birth-Weight Infant Postdischarge
Bibliographic record
Abstract
Gastroesophageal reflux and gastroesophageal reflux disease symptoms are common challenges for very low-birth-weight infants (<1500 g). These symptoms frequently result in feeding difficulties and family stress. Management of symptoms across healthcare disciplines may not be based on current evidence, and inconsistency can result in confusion for families and delayed interventions. The feeding relationship between infant and caregivers may be impaired when symptoms are persistent and poorly managed. An algorithm for managing gastroesophageal reflux-like symptoms in very low-birth-weight infants (from hospital discharge to 12 months corrected age) was developed through the formation of a multidisciplinary community of practice and critical appraisal of the literature. A case study demonstrates how the algorithm results in a consistent approach for identifying symptoms, applying appropriate management strategies, and facilitating appropriate timing of medical consultation. Application to managing gastroesophageal reflux symptoms in the neonatal intensive care unit will be briefly addressed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".