Efficacy of Video Laryngoscopy versus Direct Laryngoscopy in the Prehospital Setting: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Introduction: Placing an endotracheal tube is a life-saving measure. Direct laryngoscopy (DL) is traditionally the default method. Video laryngoscopy (VL) has been shown to improve efficiency, but there is insufficient evidence comparing VL versus DL in the prehospital settings. This study, comprising a systematic review and random-effects meta-analysis, assesses current literature for the efficacy of VL in prehospital settings. Methods: PubMed and Scopus databases were searched from their beginnings through March 1, 2022 for eligible studies. Outcomes were the first successful intubation, overall success rate, and number of total DL versus VL attempts in real-life clinical situations. Cochrane’s Risk of Bias (RoB) tool and the Newcastle-Ottawa Scale (NOS) were applied to assess risk of bias and study quality; Q-statistics and I2 values were used to assess heterogeneity. Results: The search yielded seven studies involving 23,953 patients, 6,674 (28%) of whom underwent intubation via VL. Compared to DL, VL was associated with a statistically higher risk ratio for first-pass success (Risk Ratio [RR] = 1.116; 95% CI, 1.005-1.239; P = .041; I2 = 87%). The I2 value for the subgroup of prospective studies was 0% compared to 89% for retrospective studies. In addition, VL was associated with higher likelihood of overall success rate (RR = 1.097; 95% CI, 1.01-1.18; P = .021; I2 = 85%) and lower mean number of attempts (Mean Difference = -0.529; 95% CI, -0.922 to -0.137; P = .008). Conclusion: The meta-analysis suggested that VL was associated with higher likelihood of achieving first-pass success, greater overall success rate, and lower number of intubation attempts for adults in the prehospital settings. This study had high heterogeneity, likely presenced by the inclusion of retrospective observational studies. Further studies with more rigorous methodology are needed to confirm these results.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 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".