Evaluation of a low-cost, 3D-printed model for bronchoscopy training
Bibliographic record
Abstract
BACKGROUND: Flexible bronchoscopy is a fundamental procedure in anaesthesia and critical care medicine. Although learning this procedure is a complex task, the use of simulation-based training provides significant advantages, such as enhanced patient safety. Access to bronchoscopy simulators may be limited in low-resource settings. We have developed a low-cost 3D-printed bronchoscopy training model. METHODS: A parametric airway model was obtained from an online medical model repository and fabricated using a low-cost 3D printer. The participating physicians had no prior bronchoscopy experience. Participants received a 30-minute lecture on flexible bronchoscopy and were administered a 15-item pre-test questionnaire on bronchoscopy. Afterwards, participants were instructed to perform a series of predetermined bronchoscopy tasks on the 3D printed simulator on 4 consecutive occasions. The time needed to perform the tasks and the quality of task performance (identification of bronchial anatomy, technique, dexterity, lack of trauma) were recorded. Upon completion of the simulator tests, participants were administered the 15-item questionnaire (post-test) once again. Participant satisfaction data on the perceived usefulness and accuracy of the 3D model were collected. A statistical analysis was performed using the t-test. Data are reported as mean values (± standard deviation). RESULTS: The time needed to complete all tasks was 152.9 ± 71.5 sec on the 1st attempt vs. 98.7 ± 40.3 sec on the 4th attempt (P = 0.03). Likewise, the quality of performance score improved from 8.3 ± 6.7 to 18.2 ± 2.5 (P < 0.0001). The average number of correct answers in the questionnaire was 6.8 ± 1.9 pre-test and 13.3 ± 3.1 post-test (P < 0.0001). Participants reported a high level of satisfaction with the perceived usefulness and accuracy of the model. CONCLUSIONS: We developed a 3D-printed model for bronchoscopy training. This model improved trainee performance and may represent a valid, low-cost bronchoscopy training tool.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".