Achieving Competency in Fiber-Optic Intubation Among Resident Physicians After Higher- Versus Lower-Fidelity Task Training: A Randomized Controlled Study
Bibliographic record
Abstract
BACKGROUND: The high-fidelity ORSIM (Airway Simulation Ltd) and the low-fidelity wooden-block fiber-optic task trainers allow users to familiarize themselves with the psychomotor skills required to manipulate the fiber-optic scope. METHODS: This single-center study aimed to compare residents' performance of fiber-optic intubation after 2 different types of task training. Twenty-four residents with experience of <8 fiber-optic intubations were randomized to either the ORSIM or a wooden-block task trainer. In a single teaching session, the resident performed 20 fiber-optic intubations on their assigned task trainer. This implied simulator competence. In the 4 months after this training, all subjects then attempted to perform a fiber-optic intubation on an American Society of Anesthesiologists (ASA) I or II anesthetized patient whose airway was preoperatively assessed as normal. The primary outcome was the cumulative sum (CUSUM) learning curves obtained as the residents trained on their respective task trainers. Secondary outcomes included: the mean time (in seconds) to perform each of the 20 fiber-optic intubations on their assigned task trainer, the total simulator training time, global rating scale score, checklist score, and time to carina when performing fiber-optic intubation on the patient. RESULTS: The CUSUM analysis showed that the ORSIM group achieved simulator competence faster. The mean time to perform fiber-optic intubation was shorter in the ORSIM group. A 2-way analysis of variance (ANOVA) test suggests that the combined effect of group (wooden-block or ORSIM) and time is statistically significant ( P < .05).Total training time (mean, 899 s ± 440 s vs 1358 s ± 405 s; 95% confidence interval [CI], 100.46-818.54; P = .01) was also significantly better in the ORSIM group.No significant difference was found between the 2 groups ( P > 0) in terms of global rating scale, checklist score, and time to reach the carina ( P >.05) when performing the fiber-optic intubation on the patient. CONCLUSIONS: ORSIM showed superiority in terms of the CUSUM learning curve in reaching competence faster in fewer attempts. There was no statistically significant difference in residents' performance when translated to clinical practice on a patient. This information should assist course directors when choosing task trainers for fiber-optic intubation training programs.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".