Gastric Residual Volume in Critically Ill Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early enteral nutrition (EN) is consistently recommended as first-line nutrition therapy in critically ill patients since it favorably alters outcome, providing both nutrition and nonnutrition benefits. However, critically ill patients receiving mechanical ventilation are at risk for regurgitation, pulmonary aspiration, and eventually ventilator-associated pneumonia (VAP). EN may increase these risks when gastrointestinal (GI) dysfunction is present. Gastric residual volume (GRV) is considered a surrogate parameter of GI dysfunction during the progression of enteral feeding in the early phase of critical illness and beyond. By monitoring GRV, clinicians may detect patients with delayed gastric emptying earlier and intervene with strategies that minimize or prevent VAP as one of the major risks of EN. The value of periodic GRV measurements with regard to risk reduction of VAP incidence has frequently been questioned in the past years. Increasing the GRV threshold before interrupting gastric feeding results in marginal increases in EN delivery. More recently, a large randomized clinical trial revealed that abandoning GRV monitoring did not negatively affect clinical outcomes (including VAP) in mechanically ventilated patients. The results have revived the discussion on the role of GRV monitoring in critically ill, mechanically ventilated patients receiving early EN. This review summarizes the most recent clinical evidence on the use of GRV monitoring in critically ill patients. Based on the clinical evidence, it discusses the pros and cons and further addresses whether GRV is a dead marker or still alive for the nutrition management of critically ill patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it