91 Transforming preterm oral feeding with innovative algorithms: Insights from a quality improvement initiative
Bibliographic record
Abstract
Abstract Background Establishing safe, efficient oral feeds for preterm infants is a crucial yet challenging milestone before NICU discharge. This process is often accompanied by heightened stress and anxiety among NICU staff and parents, as nonlinear feeding progression and a lack of standardized protocols add uncertainty. Without a structured approach, feeding practices rely heavily on individual experience, leading to inconsistent care and increased caregiver stress. This quality improvement initiative at Sunnybrook Health Sciences Centre, a tertiary perinatal care unit in Toronto, Ontario, was conducted to address these challenges and to improve the transfer of knowledge around preterm oral feeding practices. Improved feeding practices lead to reduced anxiety, smoother transitions to home care, and better developmental outcomes for preterm infants. Objectives The primary aim was to create a structured, evidence-based approach to oral feeding that is adaptable to both breast and bottle feeding, reduces variability, and ensures consistent practices across all caregivers. This structured approach not only promotes safer and more effective feeding but also supports caregivers, including parents, by providing clear, actionable guidance. Design/Methods The COVID-19 pandemic highlighted a pressing need for knowledge transfer in feeding practices, as high staff turnover introduced variability in the NICU. In 2020, the multidisciplinary Sunnybrook Feeding Committee was established, comprising physicians, nurses, nurse practitioners, occupational therapists, and dietitians. Initial staff training used Supporting Oral Feeding in Fragile Infants (SOFFI®) modules to create a consistent understanding of feeding principles. With guidance from the SOFFI® creator (Consultant), our committee adapted these modules to develop two specific, tailored algorithms: the Oral Feeding Readiness Algorithm and the Oral Feeding Challenges Algorithm. Approximately 80 NICU staff members participated in surveys and focus groups, offering qualitative feedback on the algorithms' impact. Data indicated that caregivers experienced a significant reduction in stress due to the clear, consistent framework provided by the algorithms. The algorithms introduced a reliable YES/NO decision flow, enabling caregivers to respond confidently to infant cues and adjust feeding practices accordingly. Results The algorithms demonstrated notable improvements in caregiver confidence, communication, and consistency in feeding practices. Staff surveys revealed that the structured protocols reduced variability clarified feeding readiness and challenged decision-making. The algorithms ensured a universal language and framework for all care providers, reducing subjective variations and providing clear guidance for safe, supportive feeding practices that align with each infant's cues. These changes reduced stress and anxiety for both staff and parents, creating a more cohesive NICU environment focused on supporting infant well-being. Conclusion This initiative represents a broader effort to transform oral feeding practices in the NICU. This structured oral feeding approach provides caregivers with tools for confident, informed oral feeding that aligns with infant cues, thereby reducing stress, facilitating a smoother transition home and enhancing outcomes for preterm infants. Moving forward, we will focus on refining the algorithms, adapting them for parental use, and developing a comprehensive program for families to support safe and consistent feeding practices.
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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 | 0.099 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".