Pediatric Nasogastric Tube Placement and Verification: Best Practice Recommendations From the NOVEL Project
Bibliographic record
Abstract
The placement of a nasogastric tube (NGT) in a pediatric patient is a common practice that is generally perceived as a benign bedside procedure. There is potential risk for NGT misplacement with each insertion. A misplaced NGT compromises patient safety, increasing the risk for serious and even fatal complications. There is no standardized method for verification of the initial NGT placement or reverification assessment of NGT location prior to use. Measurement of the acidity or pH of the gastric aspirate is the most frequently used evidence-based method to verify NGT placement. The radiograph, when properly obtained and interpreted, is considered the gold standard to verify NGT location. However, the uncertainty regarding cumulative radiation exposure related to radiographs in pediatric patients is a concern. To minimize risk and improve patient safety, there is a need to identify best practice and to standardize care for initial and ongoing NGT location verification. This article provides consensus recommendations for best practice related to NGT location verification in pediatric patients. These consensus recommendations are not intended as absolute policy statements; instead, they are intended to supplement but not replace professional training and judgment. These consensus recommendations have been approved by the American Society for Parental and Enteral Nutrition (ASPEN) Board of Directors.
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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 | 0.038 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".