A randomised controlled trial comparing the GlideScope <sup>®</sup> and the Macintosh laryngoscope for double‐lumen endobronchial intubation
Bibliographic record
Abstract
Double-lumen endobronchial tubes are the most common method of achieving lung isolation and one-lung ventilation during thoracic anaesthesia and surgery. We compared the clinical performance of the Macintosh laryngoscope and the GlideScope(®) during endobronchial intubation with a double-lumen tube. Seventy patients with no predictors for difficult laryngoscopy were allocated randomly to the Macintosh laryngoscope or GlideScope. The time taken for endobronchial intubation with the Macintosh laryngoscope was significantly shorter compared with that taken for the GlideScope, median (IQR [range]) 33 (22-52 [11-438]) s vs 70 (39-129 [21-242]) s, respectively, p = 0.0013. There was no statistical difference in the rate of success at the first attempt (91% vs 83%, respectively). On a numerical rating scale (scored from 0 to 10), the 30 anaesthetists who took part in the study rated endobronchial intubation overall as easier using the Macintosh compared with the GlideScope, 2 (1-3 [0-8]) vs 3 (2-6 [0-10]), respectively, p = 0.003. Postoperative voice changes were also less common in the Macintosh group (8 (22%) vs 17 (58%), p = 0.045). Anaesthetists found the GlideScope more difficult to use than the Macintosh laryngoscope and endobronchial intubation took longer; therefore, we cannot recommend its routine use with double-lumen tubes in patients who are predicted to have a normal airway.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".