Diagnostic Accuracy of Lung Ultrasound in Determining the Position of the Endotracheal Tube in Mechanically Ventilated Children
Bibliographic record
Abstract
Introduction: Due to their short and narrow airways, endotracheal intubation in children is difficult and demands a high degree of precision. Chest X-rays (CXR) are the current gold standard for endotracheal tube (ETT) verification. Objective: The aim of the current study was to evaluate the role of lung ultrasonography in confirming ETT installation in children receiving mechanical ventilation. Patients and methods: A cross-sectional study was carried out at pediatric intensive care unit (PICU), Faculty of Medicine, Zagazig University Children Hospitals. A total of 30 patients, aged between 28 days and 16 years were enrolled in the study. History taking and general clinical examination were performed on all patients. The ETT was inserted and the position is adjusted by the guide of ultrasonography (Alpinion E Cube i7). Thereafter, all patients were subjected to CXR. Results: Incidence of correct ETT position with CXR was 53.3% compared to 86.7% with ultrasound (P=0.002). Agreement between ETT position detected by CXR and ETT position detected by ultrasound had a fair level. Only 17 (56.7%) children were survived after endotracheal intubation and 13 (43.3%) died among the 30 studied children. Percent of children’s death among children of once intubation was 10%, while percent of children’s death among repeated intubation was 33.3%. Conclusion: Lung ultrasonography can be used as a fast, safe and effective tool for confirming the correct placement of ETT in mechanically ventilated children, especially in conditions where CXR and capnography are not reliable or inaccessible.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".