Development of an Observational Instrument to Assess Gastro-Esophageal Reflux Disease in Premature Infants
Bibliographic record
Abstract
Background: Premature infants are at increased risk of developing Gastroesophageal Reflux Disease (GERD), which for these children is associated with a number of severe symptoms. There is great need for effective instruments and clear symptom criteria to assess the presence and degree of severity of GERD. Aim: To develop and pilot test an observation instrument for early detection of symptoms of GERD in premature infants. Method: A combination of three research methods was used – systematic literature review, observation instrument development and a pilot test. Results: The systematic review identified specific symptoms of GERD. The development of the observational instrument started with the establishment of concordance between the criteria of symptoms according to the literature review and to NIDCAP, “Newborn Individualized Developmental Care and Assessment Program”. In the pilot test the criteria of symptoms were revised by comparing the result and the criteria between infants that clinically were estimated to have had a reflux problem and the ones who did not. Conclusion: An observation instrument was developed. The clinical evaluation by a pilot test showed that the instrument could be useful to record significant symptoms and combinations of symptoms that may occur in premature infants assessed as having reflux problems.
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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 | 0.054 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".