A Clinical Nurse Specialist–Led Initiative to Reduce Deficits in Tube Feeding Administration for the Surgical and Trauma Populations
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: The purpose of this clinical nurse specialist-led initiative was to redefine the standard of care to reduce the deficit that exists between the daily amount of tube feedings prescribed versus received by patients in a surgical-trauma intensive care unit. DESCRIPTION OF THE PROJECT: Nutrition plays a vital role in health and wellness. Although nutritional recommendations are not always met by individuals on a daily basis-the presence of in-hospital malnutrition presents greater risks and complications after a surgery or traumatic event. An evidence-based algorithm for initiating and maintaining tube feedings was developed and incorporated into morning bedside report. A preintervention and postintervention chart analysis was done to calculate the amount of tube feedings received by patients during their first 5 days of admission. OUTCOMES: Preintervention data revealed that 29 patients received a mean 49.8% (SD, 21.6%) of tube feedings prescribed, and postintervention data showed 31 patients received 60.4% (SD, 18.5%) of tube feedings prescribed (P = .04). CONCLUSION: Through the implementation of a tube feeding algorithm, there was a reduction of tube feed interruptions and volume deficits during the first 5 days of admission.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".