Correlation between gastric residual volumes and markers of gastric emptying: A post hoc analysis of a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: The correlation between gastric residual volumes (GRVs) and markers of gastric emptying (GE) in critically ill patients is unclear. This particularly applies to critically ill surgical patients, as they are underrepresented in previous studies. METHODS: We conducted a post hoc analysis of a multicenter trial that investigated the effectiveness of a promotility drug. Pharmacokinetic markers of GE (3-O-methylglucose [3-OMG] and acetaminophen) were correlated with GRV measurements. High GRV was defined as one episode of >400 ml or two consecutive episodes of >250 ml, and delayed GE was defined as <20th percentile of the pharmacokinetic GE marker that had the strongest correlation with GE. RESULTS: Of 77 patients, 8 (10.4%) had high GRV, and 15 (19.5%) had delayed GE. The 3-OMG concentration at 60 min had the strongest correlation with GRV (ρ = -0.631), and high GRV had low sensitivity (46.7%) but high specificity (98.4%) in discriminating delayed GE. The positive (87.5%) and negative (88.4%) predictive values were similar. Compared with medical patients, surgical patients (n = 14, 18.2%), had a significantly higher incidence of high GRV (29% vs 6%, P = .032) and a trend toward delayed GE (36% vs 16%, P = .132). CONCLUSION: GRV reflects GE, and high GRV is an acceptable surrogate marker of delayed GE. From our preliminary observation, surgical patients may have a higher risk of high GRV and delayed GE. In summary, GRV should be monitored to determine whether complex investigations or therapeutic interventions are warranted.
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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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".