The Flex-It™ Stylet Is Less Effective than a Malleable Stylet for Orotracheal Intubation Using the GlideScope®
Bibliographic record
Abstract
BACKGROUND: The GlideScope videolaryngoscope (Verathon Medical, Bothell, WA) usually provides excellent glottic visualization, but directing an endotracheal tube through the vocal cords can be challenging. The goal of the study was to compare the dedicated Flex-It stylet (FIS, Parker Medical, Highlands Ranch, CO) with a malleable stylet, as assessed by time to intubation (TTI). METHODS: Eighty patients requiring orotracheal intubation for elective surgery were randomly allocated to either the FIS or a malleable stylet (control) to facilitate tracheal intubation using the GlideScope. TTI was recorded by blinded assessors; operators were blinded until after laryngoscopy. The operator assessed the ease of intubation using a 100-mm visual analog scale (0 = easy to 100 = difficult). The number of intubation attempts, number of failures, glottic grades, and use of external laryngeal manipulation were documented. RESULTS: The median TTI was 41 s (interquartile range [IQR] 30-51) for the Flex-It group compared with 32 s (IQR 28-42) for the control group (P = 0.03). The median visual analog scale score for ease of intubation was 20 (IQR 11-39) for the Flex-It group compared with 15 (IQR 8-28) for the control group (P = 0.13). The overall incidence of a Cormack-Lehane Grade I or II glottic view was 100%. CONCLUSIONS: In a group of experienced operators using the GlideScope, the FIS was less effective for orotracheal intubation than a malleable endotracheal tube stylet.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".