Predictive Value of Tracheal Rapid Ultrasound Exam Performed in the Emergency Department for Verification of Tracheal Intubation
Bibliographic record
Abstract
Verification of the correct placement of the endotracheal tube (ETT) has been one of the most challenging issues of airway management in the field of emergency medicine. Early detection of esophageal intubation through a reliable method is important for emergency physicians. The aim of this study was to assess the diagnostic accuracy of tracheal rapid ultrasound exam (TRUE) to assess endotracheal tube misplacement during emergency intubation. This was an observational prospective study performed in the emergency department of the major tertiary referral hospital of the city. We included a consecutive selection of 100 patients. TRUE was performed for all these patients, and subsequently, quantitative waveform capnography was done. The later test is considered as the gold standard. From our total 100 eligible patients, 93(93%) participants had positive TRUE results (tracheal intubation) and 7 (7%) patients have negative TRUE results (esophageal intubation). Quantitative waveform capnography report of all 93 (100%) patients who had positive TRUE were positive (appropriate tracheal placement). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of TRUE for detecting appropriate tracheal placement of ETT were 98.9% (95% CI, 93.3% to 99.8%), 100% (95% CI, 51.6% to 100%), 100% (95% CI, 95.1% to 100%) and 85.7% (95% CI, 42% to 99.2%) respectively. Performing TRUE is convenient and feasible in many emergency departments and pre-hospital settings. We would recommend emergency units explore the possibility of utilizing TRUE as a method in the assessment of proper ETT placement.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".