Glidescope® video-laryngoscopy versus direct laryngoscopy for endotracheal intubation: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The Glidescope(®) video-laryngoscopy appears to provide better glottic visualization than direct laryngoscopy. However, it remains unclear if it translates into increased success with intubation. METHODS: We systematically searched electronic databases, conference abstracts, and article references. We included trials in humans comparing Glidescope(®) video-laryngoscopy to direct laryngoscopy regarding the glottic view, successful first-attempt intubation, and time to intubation. We generated pooled risk ratios or weighted mean differences across studies. Meta-regression was used to explore heterogeneity based on operator expertise and intubation difficulty. RESULTS: We included 17 trials with a total of 1,998 patients. The pooled relative risk (RR) of grade 1 laryngoscopy (vs ≥ grade 2) for the Glidescope(®) was 2.0 [95% confidence interval (CI) 1.5 to 2.5]. Significant heterogeneity was partially explained by intubation difficulty using meta-regression analysis (P = 0.003). The pooled RR for nondifficult intubations of grade 1 laryngoscopy (vs ≥ grade 2) was 1.5 (95% CI 1.2 to 1.9), and for difficult intubations it was 3.5 (95% CI 2.3 to 5.5). There was no difference between the Glidescope(®) and the direct laryngoscope regarding successful first-attempt intubation or time to intubation, although there was significant heterogeneity in both of these outcomes. In the two studies examining nonexperts, successful first-attempt intubation (RR 1.8, 95% CI 1.4 to 2.4) and time to intubation (weighted mean difference -43 sec, 95% CI -72 to -14 sec) were improved using the Glidescope(®). These benefits were not seen with experts. CONCLUSION: Compared to direct laryngoscopy, Glidescope(®) video-laryngoscopy is associated with improved glottic visualization, particularly in patients with potential or simulated difficult airways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".