Impact of the 2022 national formula shortage on clinical decision-making of healthcare providers in switching amino acid formulas for infants with cow’s milk protein allergy: a survey-based study
Bibliographic record
Abstract
Background In 2022, the United States experienced a national shortage of infant formula due to a global supply chain crisis and a large-scale domestic formula recall. The existing literature on healthcare providers' (HCPs) clinical decision-making during formula shortages is limited. This study aims to analyze the factors influencing pediatric HCP clinical decision-making when switching between amino acid formulas (AAF) for managing cow's milk protein allergy (CMPA) in infants under 24 months of age during an unprecedented national formula shortage. Methods The study included pediatric HCPs with experience managing CMPA in infants and toddlers under 24 months during the formula shortage from January 2022 to November 2022. A de-identified survey comprising 26 questions examining driving factors used in clinical decision-making was administered to pediatric HCPs using a real-time mobile data collection tool. Results Among the surveyed pediatric HCPs ( n = 75), the factors most frequently considered as “extremely important” when switching to another AAF included safety (85%), tolerability (73%), and efficacy (83%). No statistically significant differences were found in HCP ratings among the listed examined factors of the four AAFs. The availability of specific formulas was the only factor that exhibited a statistically significant difference in perceived performance among pediatric HCPs when comparing the four AAFs ( p < 0.05). Discussion This study elucidates the crucial aspects that influenced pediatric HCPs' selection of AAFs for CMPA management during the 2022 formula shortage. The findings highlight the significance of safety, tolerability, efficacy, and availability in the pediatric HCP decision-making processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".