The Impact of Remote Visualized Teaching on Clinical Competence Following Short-Term Bronchoscopy Training
Bibliographic record
Abstract
Shupeng Wang,1 Dejing Song,1 Xiaocong Sun,1 Chen Li,1 Hui Wang,1 Junyu Ma,1 Wei Li2 1Intensive Care Unit, China-Japan Friendship Hospital, Beijing, People’s Republic of China; 2Health Check-up Center, China-Japan Friendship Hospital, Beijing, People’s Republic of ChinaCorrespondence: Wei Li, Health Check-up Center, China-Japan Friendship Hospital, Beijing, People’s Republic of China, Tel +86 15652749107, Email web_document@163.comObjective: To evaluate the impact of remote visualized teaching (RVT) on trainees’ confidence and clinical competence after short-term traditional simulation-based bronchoscopy training.Methods: In this prospective self-controlled quasi-experimental study, two cohorts, with 24 trainees each, completed a one-day traditional bronchoscopy course and voluntarily joined a one-month RVT program. Confidence and clinical competence were evaluated before and after RVT using the Bronchoscopy Operator Confidence Scale (BOCS) and a modified Ontario Bronchoscopy Assessment Tool (OBAT), with scores analyzed using the Wilcoxon signed-rank test.Results: 48 trainees from 43 hospitals (81.2% secondary-level) completed the RVT course. Median BOCS scores increased significantly from 60.0 (54.0– 64.0) to 75.0 (72.0– 81.0; p< 0.001), with notable improvements in emergency response (2.00→ 3.50) and operational skills (2.75→ 3.50). Modified OBAT scores rose from 66.7 (60.7– 74.4) to 79.7 (76.7– 84.9; p< 0.001), notably, there were significant improvements in the scores for operational skills, diagnostic abilities, and post-procedure management.Conclusion: Remote visualized teaching significantly enhances trainees’ confidence and clinical competence, serving as a valuable adjunct to traditional bronchoscopy education.Keywords: bronchoscopy, training, remote visualized teaching, clinical competence
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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 | 0.002 | 0.010 |
| 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) | 0.002 | 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 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".