Patient- and Operator-Related Factors Associated with Successful Glidescope® intubations: A Prospective Observational Study in 742 Patients
Bibliographic record
Abstract
The Glidescope Video Laryngoscope (Glidescope, Verathon Medical, Bothell, WA, U.S.A.) is a relatively new intubating device. It has been proposed to be useful for securing both routine airways and those where direct laryngoscopy may be difficult. In this prospective observational study, data for 742 intubations using the Glidescope were collected to investigate whether four factors are associated with successful tracheal intubation at first attempt using the Glidescope: previous Glidescope experience, previous direct laryngoscopy experience, level of anaesthesia training and clinical airway assessment. The likelihood of successful tracheal intubation at first attempt using the Glidescope increased with increasing previous Glidescope experience. Similarly, success was more likely in airways that were assessed as normal compared with those where direct laryngoscopies were either predicted or known to be difficult. Subgroup analysis indicated 83% first attempt success by 'experienced' Glidescope users in patients with documented difficult direct laryngoscopies. This supports its use as an adjunct device for management of airways where direct laryngoscopies prove difficult. With regard to the level of anaesthesia training, only medical students were more likely to fail with the Glidescope. Success was not associated with previous experience in direct laryngoscopy. The lack of association with direct laryngoscopy experience and level of anaesthesia training (beyond student level) suggests that expertise with traditional airway tools is not necessary to become proficient with the Glidescope.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".