Comparing the First-Pass Success Rate of the King LTS-D and the i-gel Airway Devices in Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
OBJECTIVES: Significant heterogeneity exists internationally in the airway devices used in the pre-hospital setting during cardiac arrest. This study evaluated the first-pass success (FPS) rate of two supraglottic airways (SGAs) used by paramedics during out-of-hospital cardiac arrest: the King LTS-D and the i-gel. METHODS: By examining 2,680 patient care records compiled by Ambulance New Brunswick between 2015 and 2020, we evaluated the FPS rate of the two SGAs using a 2x2 Pearson chi-square test for association, and a Mann-Whitney U test, to determine whether there were significant differences in FPS rates. RESULTS: (1) = 96.01, p < 0.001. The odds of successfully inserting the airway on the first attempt were 2.94 times higher if paramedics used the i-gel than if they used the King LTS-D with a 95% CI [2.32, 3.60]. Mann-Whitney's U test for SGA differences favored the i-gel in fewer attempts for successful insertion (z = -4.357, p < 0.001, d = 0.15). CONCLUSIONS: Among patients in New Brunswick with out-of-hospital cardiac arrest, paramedics had a higher FPS rate with the i-gel compared to the King LTS-D. Our study also found a statistically significant difference between the King LTS-D and i-gel, favoring the i-gel in fewer attempts. Our findings suggest that the i-gel provides a more consistent FPS rate compared to the King LTS-D within our study populations; however, further research is necessary to determine the clinical implications of this. While multiple attempts at tracheal intubation are associated with negative clinical outcomes, no such evidence exists for SGAs.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".