Fiberoptic Orotracheal Intubation on Anesthetized Patients
Bibliographic record
Abstract
BACKGROUND: With increasing pressure to use operating room time efficiently, opportunities for residents to learn fiberoptic orotracheal intubation in the operating room have declined. The purpose of this study was to determine whether fiberoptic orotracheal intubation skills learned outside the operating room on a simple model could be transferred into the clinical setting. METHODS: First-year anesthesiology residents and first- and second-year internal medicine residents were recruited. Subjects were randomized to a didactic-teaching-only group (n = 12) or a model-training group (n = 12). The didactic-teaching group received a detailed lecture from an expert bronchoscopist. The model-training group was guided, by experts, through tasks performed on a simple model designed to refine fiberoptic manipulation skills. After the training session, subjects performed a fiberoptic orotracheal intubation on healthy, consenting, anesthetized, paralyzed female patients undergoing elective surgery with predicted "easy" laryngoscopic intubations. Two blinded anesthesiologists evaluated each subject. RESULTS: After the training session, the model group significantly outperformed the didactic group in the operating room when evaluated with a global rating scale (P < 0.01)and checklist (P0.05). Model-trained subjects completed the fiberoptic orotracheal intubation significantly faster than didactic-trained subjects (P < 0.01). Model-trained subjects were also more successful at achieving tracheal intubation than the didactic group (P < 0.005). CONCLUSION: Fiberoptic orotracheal intubation skills training on a simple model is more effective than conventional didactic instruction for transfer to the clinical setting. Incorporating an extraoperative model into the training of fiberoptic orotracheal intubation may greatly reduce the time and pressures that accompany teaching this skill in the operating room.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".