Laryngoscopic Intubation
Bibliographic record
Abstract
BACKGROUND: Many healthcare professionals are trained in direct laryngoscopic tracheal intubation (LEI), which is a potentially lifesaving procedure. This study attempts to determine the number of successful LEI exposures required during training to assure competent performance, with special emphasis on defining competence itself. METHODS: Analyses were based on a longitudinal study of novices under training conditions in the operating room. The progress of 438 LEIs performed by the 20 nonanesthesia trainees was monitored by observation and videotape analysis. Eighteen additional LEIs were performed by experienced anesthesiologists to define the standard. A generalized linear, mixed-modelling approach was used to identify key aspects of effective training and performance. The number of tracheal intubations that the trainees were required to perform before acquiring expertise in LEI was estimated. RESULTS: Subjects performed between 18 and 35 laryngoscopic intubations. However, statistical modeling indicates that a 90% probability of a "good intubation" required 47 attempts. Proper insertion and lifting of the laryngoscope were crucial to "good" or "competent" performance of LEI. Traditional features, such as proper head and neck positions, were found to be less important under the study conditions. CONCLUSIONS: This study determined that traditional LEI teaching for nonanesthesia personnel using manikin alone is inadequate. A reevaluation of current standards in LEI teaching for nonanesthesia is required.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".