Managing airway by supra-or retroglottic devices during rotary wings MED/CASEVAC : a systematic review
Bibliographic record
Abstract
Introduction : Airway patency is crucial during life threatening events. Various devices can be used to keep the airway secure, such as the supraglottic devices (SAD). This group of devices consists of a variety of different types. Managing the airway in a prehospital setting can be challenging. Not to mention the impeding factors in in-flight situations, such as vibrations, lack of luminosity, cuff pressure changes and lack of expertise. This systematic literature review is aiming to determine which SAD is the most suitable for the prehospital MED/CASEVAC setting. Method : A search in PubMed, Embase, Google Scholar, Cochrane and ScienceDirect was performed. An additional search was done using the references of the review articles. One reviewer selected relevant articles after title, abstract and full text screening. Quality of the studies was assessed with the Newcastle Ottawa quality assessment scale. Results : In total, 4097 records were screened, of which 107 were included. The primary outcomes studied were technique (insertion time, number of insertion attempts, ease of insertion, manual maneuvers, ETI through the SAD, gastric tube insertion, learning effect, preference), monitoring (hemodynamic parameters, fiberoptic control, oropharyngeal leak pressure, peak airway pressure, oxygen saturation, ETCO2), safety (success rate, complications, mortality, cuff pressure) and specific difficulties of the prehospital setting. Each feature in the results has been divided in studies performed on humans and manikins. The human group was split in hospital setting, with ASA I-II patients, and pre-hospital setting. Discussion : Based on the available literature and considering the quality analysis the i-gel seems to be superior when looking at insertion time, ease of insertion, insertion time of ETT, fiberoptic control and complications. The highest oropharyngeal leak pressure (OLP) was found with pLMA. The fLMA seems to be superior regarding success rate of ETI. The i-gel was the preferred device by the users, except for ETI through the SAD, where fLMA was favored. Most included studies were conducted in a controlled hospital environment or on manikins. Therefore, more studies are needed, especially in real flying conditions and with different devices to facilitate comparisons.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".