O-042 Gastric Residuals In Preterm Infants As Predictor Of Tolerance To Early Enteral Feeds (grip Trial)
Bibliographic record
Abstract
Background Evidence is inconsistent to support checking gastric residual volumes (GRv) in predicting feeding intolerance in preterm infants. GRv remains standard practice in guiding feeding advancement in several neonatal centres. We hypothesises that this practice delays establishment of full enteral feeding with associated complications. Aims The effect on time to reach full feeds (120 ml/k/day) with not checking GRv in advancing feeds in preterm infants. Methods Design Single Centre, unmasked, parallel armed RCT Inclusion criteria Infants recruited within 48hrs of birth with birth weight (BW) ≥1500 grams ≤ 2000 grams. Exclusion criteria Major congenital malformations, asphyxia and BW ≤3rd percentile Randomization Variable number blocks stratified by BW Study intervention GRv assessed only with bloody aspirates or with vomiting and abnormal abdominal examination. Control GR volume assessed routinely with feeding advancement Results 86 infants with BW 1750 ± 140 g and gestational age 32.1 ± 1.5 weeks were enrolled. There was no difference in time to reach full feeds with both groups. Enteral feeds 120 mL/kg/d were achieved at DOL 5.9 ± 1.7 and 5.7 ± 1.8 in study and control group respectively. There was no difference in episodes of feeding interruptions, incidence of sepsis, reaching BW, and 120% of BW between two groups. However, two infants in the control group developed NEC. Conclusions Not checking GRv while advancing feeds in late preterm infants did not statistically reduce the time to achieve full enteral feeds however there were no adverse events noted with this practice. This study should be done in VLBW babies where GRv is a major hurdle to feeding advancement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".