Simulation-based training in flexible fibreoptic intubation
Bibliographic record
Abstract
BACKGROUND: Flexible fibreoptic intubation (FOI) is a key element in difficult airway management. Training of FOI skills is an important part of the anaesthesiology curriculum. Simulation-based training has been shown to be effective when learning FOI, but the optimal structure of the training is debated. The aspect of dividing the training into segments (part-task training) or assembling into one piece (whole-task training) has not been studied. OBJECTIVE: The aims of this study were to compare the effect of training the motor skills of FOI as part-task training or as whole-task training and to relate the performance levels achieved by the novices to the standard of performance of experienced FOI practitioners. DESIGN: A randomised controlled study. SETTING: Centre for Clinical Education, University of Copenhagen and the Capital Region of Denmark, between January and April 2013. PARTICIPANTS: Twenty-three anaesthesia residents in their first year of training in anaesthesiology with no experience in FOI, and 10 anaesthesia consultants experienced in FOI. INTERVENTIONS: The novices to FOI were allocated randomly to receive either part-task or whole-task training of FOI on virtual reality simulators. Procedures were subsequently trained on a manikin and assessed by an experienced anaesthesiologist. The experienced group was assessed in the same manner with no prior simulation-based training. MAIN OUTCOME MEASURES: The primary outcome measure was the score of performance on testing FOI skills on a manikin. RESULTS: A positive learning effect was observed in both the part-task training group and the whole-task training group. There was no statistically significant difference in final performance scores of the two novice groups (P = 0.61). Furthermore, both groups of novices were able to improve their skill level significantly by the end of manikin training to levels comparable to the experienced anaesthesiologists. CONCLUSION: Part-task training did not prove more effective than whole-task training when training novices in FOI skills. FOI is very suitable for simulation-based training and segmentation of the procedure during training is not necessary.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".