Bibliographic record
Abstract
The difficult airway continues to challenge anesthesiologists. Recently, the development of laryngoscopes that have video cameras built in has led to some improvement in visualization of airway anatomy. One such device is the GlideScope®1(Saturn Biomedical Systems, Burnaby, British Columbia, Canada). It is equipped with a patent antifogging system that, together with a design that tends to keep the camera free of blood and secretions, has made visualization of airway structures better. However, despite better glottic visualization, on some occasions the endotracheal tube may still be difficult to pass into the larynx.We recently provided general anesthesia to an obese female patient, aged 32 yr, weighing 142 kg, with a Mallampati class 4 airway. The patient had a short neck with a hyomental space of three finger-breadths. We chose to use the GlideScope® to facilitate the intubation. Although the camera revealed a class II view (only a portion of the vocal cords were visualized), it was impossible to maneuver the endotracheal tube into the laryngeal opening even using the stylet supplied by the manufacturer of the GlideScope®. We then removed the stylet while leaving the endotracheal tube tip still visible in the GlideScope® monitor. We threaded a fiberoptic scope through the endotracheal tube until its tip also became visible on the GlideScope® monitor. Then, by using the thumb lever on the fiberoptic scope to control the tip, we managed to pass the fiberoptic scope through the vocal cords into the trachea and then pass the tube over the scope. In essence, the fiberoptic endoscope provides a “controllable stylet” to facilitate entry into the airway.*University of California, Irvine, California. msmoore@uci.edu
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".