Fiberoptic Oral Intubation
Bibliographic record
Abstract
BACKGROUND: Previous studies have indicated that fiberoptic orotracheal intubation (FOI) skills can be learned outside the operating room. The purpose of this study was to determine which of two educational interventions allows learners to gain greater capacity for performing the procedure. METHODS: Respiratory therapists were randomly assigned to a low-fidelity or high-fidelity training model group. The low-fidelity group was guided by experts, on a nonanatomic model designed to refine fiberoptic manipulation skills. The high-fidelity group practiced their skills on a computerized virtual reality bronchoscopy simulator. After training, subjects performed two consecutive FOIs on healthy, anesthetized patients with predicted "easy" intubations. Each subject's FOI was evaluated by blinded examiners, using a validated global rating scale and checklist. Success and time were also measured. RESULTS: Data were analyzed using a two-way mixed design analysis of variance. There was no significant difference between the low-fidelity (n = 14) and high-fidelity (n = 14) model groups when compared with the global rating scale, checklist, time, and success at achieving tracheal intubation (all P = not significant). Second attempts in both groups were significantly better than first attempts (P < 0.001), and there was no interaction between "fidelity of training model" and "first versus second attempt" scores. CONCLUSIONS: There was no added benefit from training on a costly virtual reality model with respect to transfer of FOI skills to intraoperative patient care. Second attempts in both groups were significantly better than first attempts. Low-fidelity models for FOI training outside the operating room are an alternative for programs with budgetary constraints.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".