Fiberoptic Orotracheal Intubation on Anesthetized Patients
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 it