Systemizing and Transforming Preterm Oral Feeding Through Innovative Algorithms
Bibliographic record
Abstract
Background: Establishing safe and efficient oral feeds for preterm infants is one of the last milestones to be achieved prior to discharge home. However, this process commonly elicits stress and anxiety in both care providers, such as nurses and the entire healthcare team in the Neonatal Intensive Care Unit (NICU), as well as parents. These feelings of uncertainty are exacerbated by the non-linear progression of oral feeding development and the absence of a systematized approach to initiate and advance feedings. Methods: In this 48-bed tertiary perinatal centre, staff surveys and a needs assessment showed dissatisfaction and increasing stress and anxiety due to the inconsistencies in initiating and advancing oral feeds. This paper describes the formation of a multidisciplinary feeding committee which reviewed various oral feeding training materials and the ultimate creation of two innovative oral feeding algorithms and their corresponding education materials. Results: The Sunnybrook Feeding Committee has developed two evidence-based algorithms, one for initiating oral feeds and another for monitoring progress with objective decision-making points during common oral feeding challenges. To complement and support these algorithms, educational materials and a comprehensive documentation process were also created. These resources included detailed instructions, visual aids, and step-by-step guides to help staff understand and apply the algorithms effectively. Additionally, the educational materials aimed to standardize training and ensure consistency across the NICU, further promoting a systematic approach to preterm oral feeding. Implementation of these algorithms also aimed to provide evidence-based, expert-guided guidelines for assessing readiness, initiating feeds, monitoring progress, and making necessary adjustments. Conclusions: This structured approach lays the foundation for a unit-wide language and systematic process for oral feeding. The next steps in this quality improvement project involve educating and piloting the implementation of the developed oral feeding algorithms, gathering staff feedback, and refining the tools accordingly. The goal is to enhance overall care quality, reduce stress for both care providers and parents, and ensure the best possible start for vulnerable preterm infants, ultimately supporting a smooth and successful transition to home.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".