Adherence to a Nurse‐Driven Feeding Protocol in a Pediatric Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Patients admitted to pediatric intensive care units (PICUs) often experience prolonged periods without nutrition support, which may result in hospital-induced malnutrition and longer length of stay. Nurse-driven feeding protocols have been developed to prevent unnecessary interruptions or delays to nutrition support. The primary objective of this study was to identify compliance and reasons for noncompliance to a feeding protocol at a tertiary care hospital PICU in Canada. The secondary aim was to determine the mean time (hours) spent without any form of nutrition and to identify reasons for time spent without nutrition. MATERIALS AND METHODS: This was a prospective cohort audit, consisting of 150 consecutive PICU admissions (January-February 2016). Exclusion criteria consisted of patient mortality within 48 hours (n = 1) and patients who were still admitted at the end of the data collection timeframe (n = 7). The remaining cohort consisted of 142 consecutive admissions. Data collection took place in real time and included patient demographics, diagnostic categories, time spent without nutrition, reasons for interruptions to nutrition support, and reasons for noncompliance to the protocol. Observations were obtained through paper and computer charts and conversing with clinicians. RESULTS: There was a 95% compliance rate to the protocol and an average of 25.6 hours spent without nutrition per patient. The most prevalent reason for noncompliance was an avoidable delay to restart feeds before/after procedures or after surgery. CONCLUSIONS: A nurse-driven feeding protocol may reduce time spent without nutrition. Future research is required to examine the relationship between adherence to feeding protocols and clinical outcomes.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".