Bibliographic record
Abstract
Editor, We thank Dr Badiger et al.1 for their interest in our article.2 Since our first study on the prototype,3 we have followed the development of the ORSIM virtual reality bronchoscopy simulator (Airway Simulation Limited, Auckland, New Zealand) and are pleased that the experiences of Dr Badiger et al. are positive with the commercially available version. Interestingly, their findings closely resemble earlier results published on the other virtual-reality simulator in our study (AccuTouch; CAE Healthcare, Montreal, Canada).4 However, it is important to acknowledge that neither trainees’ self-assessed learning gain nor their perception of simulation realism are valid measures of the efficacy of the training. Literature shows that we cannot trust self-assessment5 and the fact that trainees feel more confident after a training intervention should not be equalled to increased competence. The importance of realism in simulation is under debate and a recent review found only a minimal relationship between simulation fidelity and transfer of learning.6 Virtual-reality simulators can show a range of difficult airway scenarios, but only the most expensive ones provide haptic feedback. Tube advancement is a key component of fibreoptic intubation and can only be practised on physical models. No evidence favours virtual-reality simulation over practising on mannequins when learning fibreoptic intubation. The best possible way of practising the procedure remains to be established. In a recent randomised controlled study,7 we compared part-task training with whole-task training and found a positive learning effect in both groups but no significant differences in fibreoptic intubation skills between groups. Future studies should compare different approaches to training and ideally use blinded assessments of real procedures as outcome measures (transfer studies). In conclusion, we agree that fibreoptic intubation should be practised in a simulated environment prior to performance on patients. Available, local resources should decide which teaching modalities to use. Acknowledgements relating to this article Assistance with the letter: none. Financial support and sponsorship: none. Conflicts of interest: none.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".