The Macintosh Laryngoscope: the Mechanism of Laryngeal Exposure and the Optimal Maneuver
Bibliographic record
Abstract
Background: The Macintosh laryngoscope enables safer and easy intubation. However, the mechanism of laryngeal exposure remains unclear. I hypothesized that extension of the hyoepiglottic ligament contributes to elevation of the epiglottis, and laryngoscope maneuvers that pull the hyoid bone rostrally will effectively elevate the epiglottis and expose the glottides. Methods: To test this hypothesis, I developed a model in which the epiglottis and hyoid bone were connected with a Velcro tape to allow flexible movement and applied different maneuvers to investigate their effects on epiglottis and hyoid bone movement. Results: A comparison of the original Macintosh maneuver, a modified Macintosh maneuver and the McCoy maneuver found that the McCoy maneuver elevated the epiglottis most, and was associated with rostral and anterior displacement of the hyoid bone. When the model was adjusted so that the hyoid bone was positioned caudal to the epiglottic vallecula, the hyoepiglottic ligament was shortened and the original Macintosh maneuver failed to elevate the epiglottis. Based on these results, a modified Macintosh maneuver was applied that elevated the hyoid bone rostrally and anteriorly and enhanced epiglottis elevation. Conclusions: The position of the hyoid bone should be considered to achieve epiglottic elevation and a good view of the glottides when performing laryngeal exposure. The position of the hyoid bone relative to the epiglottic vallecula could determine the response of the hyoepiglottic ligament and epiglottis elevation. Increased understanding of the mechanism of laryngeal exposure enables development of improved intubation devices and training models. J Curr Surg. 2018;8(1-2):1-6 doi: https://doi.org/10.14740/jcs348w
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".