Managing cow's milk protein allergy during the 2022 formula shortage: decision-making among pediatric healthcare providers
Bibliographic record
Abstract
Introduction: Cow's milk protein allergy (CMPA) affects 2%-7% of infants and is managed with hypoallergenic formulas. The 2022 recalls of infant formulas due to factors including contamination led to specialty formula shortages, highlighting CMPA management challenges. Understanding healthcare providers' (HCPs) decision-making in transitioning to alternative formulas during shortages is crucial. Limited attention has been given to how pediatric physicians make these choices. Methods: This study utilized US HCPs' de-identified survey data to assess driving factors when switching extensively hydrolyzed formulas during shortages. Results: 104 eligible HCPs participated, including general pediatrics, pediatric allergy/immunology, and pediatric gastroenterology specialists. Safety, tolerability, and efficacy were identified as top factors for switching formulas. Formula 1 was considered well-tolerated, patient-accepted, and safe by all HCPs. Most expressed strong belief in Formula 1's safety and effectiveness. Discussion: Findings inform CMPA management during shortages, offering guidance to HCPs for suitable formula selection and enhanced infant care.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".