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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".