Why USB-endoscope laryngoscopy is as effective as video laryngoscopy
Bibliographic record
Abstract
PURPOSE: To compare the efficacy of a low-cost custom-made universal serial bus (USB) endoscope laryngoscope for intubation with a direct laryngoscope and a high-cost video laryngoscope in a mannequin study. METHODS: We used one intubation simulator model (mannequin) in our study. A USB endoscope was mounted to the direct laryngoscope as a custom-made USB endoscope laryngoscope (USB-L). We used a video laryngoscope (Glidescope®, Verathon, USA) and a direct laryngoscope (Macintosh) for comparison. Intubation time and the correct placement of the tube were measured. Intubations were performed by two operators and results were compared. RESULTS: We found a statistically significant difference between the video and direct laryngoscope groups (p < 0.001), as well as between the USB-L and direct laryngoscope groups (p = 0.001) for Operator 1. For Operator 2, there was a statistically significant difference between the video laryngoscope group and the direct laryngoscope group (p = 0.022); however, we did not find a significant difference between the USB-L group and the direct laryngoscope group (p = 0.154). Furthermore, there were no significant differences between the USB-L and video laryngoscope groups for either operator (p=0.347 for Operator 1 and p>0.999 for Operator 2). CONCLUSION: Our study showed that USB endoscope laryngoscope provided similar intubation time to video laryngoscopy at a fraction of the cost; and both had superior times in comparison with direct laryngoscopy.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".