Factors Associated with Interruptions of Enteral Nutrition and the Impact on Macro- and Micronutrient Deficits in ICU Patients
Bibliographic record
Abstract
Background and Aim: Feeding interruptions in critical care patients are often unjustified. We aimed to determine the causes, duration, and frequency of enteral nutrition interruptions (ENIs) and to assess macronutrients and antioxidant deficits according to European Society of Parenteral Enteral Nutrition (ESPEN) guidelines. Methods: We prospectively enrolled Intensive Care Unit (ICU) patients admitted for more than 48 h with an inability to orally eat from April to December 2019. The type of enteral nutrition, the number of calories administered, the time of feeding initiation, the reasons for delaying feeding, and the causes for ENI were recorded. Results: 81 patients were enrolled, with a median duration of ENIs of 5.2 (3.4–7.4) hours/day. Gastric residual volume (GRV) monitoring—a highly controversial practice—was the most common cause of ENI (median duration 3 (2.3–3) hours/day). The mean energy intake was 1037 ± 281 kcal/day, while 60.5% of patients covered less than 65% of the total energy needs (1751 ± 295 kcal/day, according to mean Body Mass Index (BMI)). The median daily protein intake did not exceed 0.43 ± 0.3 gr/kg/day of the actual body weight (BW), whereas ESPEN recommends 1.3 gr/kg/day for adjusted BW (p < 0.001). The average administration of micronutrients and antioxidants (arginine, selenium, zinc, vitamins) was significantly less than the dietary reference intake (p < 0.01). Conclusion: ENIs lead to substantial caloric, protein, and antioxidant deficits.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".