Methods of Gastric Tube Placement Verification in Neonates, Infants, and Children: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: The objective was to evaluate diagnostic performance of multiple methods used to assess gastric tube placement verification in neonates, infants, and children. METHODS: A systematic review using the methods outlined in the Cochrane Handbook for Reviews of Diagnostic Test Accuracy was conducted. Eight databases were searched. Studies on neonates, infants, and children in which researchers compared different methods for gastric tube placement verification with x-ray reference standard were eligible in the review. RESULTS: Eight studies involving 911 participants that evaluated 9 index tests for gastric tube placement verification were included. Most studies were of moderate methodological quality, and most index tests were assessed in small individual studies. pH testing with cutoff values ≤ 6 for gastric tube position confirmation was the only index test subjected to meta-analysis, with the summary sensitivity and specificity being 0.77 (95% confidence interval [CI] 0.56-0.90) and 0.42 (95% CI 0.16-0.73). Other tests for gastric tube placement verification showed great variations in sensitivities and specificities. DISCUSSION: pH ≤ 6 is not sufficiently accurate to be recommended for gastric tube placement verification in neonates, infants, and children. Diagnostic performance of pH ≤ 4 or 5 and other methods cannot be determined because of the paucity of data and methodological variations in studies. Clinical practice related to the diagnostic tests used will continue to be dictated by local preferences and cost factors, until stronger evidence becomes available.
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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 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".