Methods for Estimating Endotracheal Tube Insertion Depth in Neonates: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Objective To systematically review the methods for estimating endotracheal tube (ETT) insertion depth in neonates. Study Design Medline, Embase, Cochrane Central, and Cumulative Index to Nursing and Allied Health Literature databases searched for randomized clinical trials (RCTs). RCTs comparing two or more different methods to estimate ETT insertion depth were included. Two co-authors independently extracted the data and assessed the risk of bias. The primary outcome includes the proportion of optimally placed ETT tips identified on chest X-ray. Results Eight RCTs evaluating seven different estimation methods were included. Trials varied defining the optimal position of the ETT tip. Overall, the percentage of optimal position ranged from 8.8 to 93%. The weight, gestation nomogram, and vocal cord estimation methods resulted in malpositioning of ETT tips in more than half of infants ≤30 weeks' gestational age. The rates of optimal ETT tip placement with the digital palpation method differ between moderately (83–93%; two RCTs) and extremely (47%; one RCT) preterm infants. Meta-analysis showed no difference between weight-based and digital palpation methods (relative risk = 0.88; 95% confidence interval = 0.75–1.04; three RCTs; participants = 205; I 2 = 0%; quality of evidence, low). Conclusion Commonly used estimation methods for ETT tip placement are inaccurate and unreliable. Further research is required to improve the accuracy of estimation methods and also to identify the usefulness of the digital palpation method in large clinical trials.
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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 | 0.040 | 0.114 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.026 | 0.035 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".