Cold Kristoffer Mazanti, Wei Wei, Agbontaen Kaladerhan, Singh Suveer, Konge Lars
Copenhagen Academy for Medical Education and Simulation (CAMES), Rigshospitalet, Capital Region of Denmark, Copenhagen, Denmark.
Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
Respiration. 2025;104(3):206-215. doi: 10.1159/000542045. Epub 2024 Oct 17.
Simulation-based training has proven effective for learning flexible bronchoscopy. However, no studies have tested the efficacy of training toward established proficiency criteria, i.e., mastery learning (ML). We wish to test the effectiveness of ML compared to directed self-regulated learning (DSRL) on novice bronchoscopists' end-of-training performance.
In a standardized simulated setting, novices without prior bronchoscopy experience were trained using an artificial intelligence (AI) guidance system that automatically recognizes the bronchial segments. They were randomized into two groups: the ML group and the DSRL group. The ML group was trained until they completed two procedures meeting the proficiency targets: 18 inspected segments, 18 structured progressions, <120-s procedure time. The DSRL group was trained until they no longer perceived any additional benefits from training. Both groups then did a finalizing test, without the AI guidance enabled.
A total of 24 participants completed the study, with 12 in each group. Both groups had a high mean number of inspected segments (ML = 17.2 segments, DSRL = 17.3 segments, p = 0.85) and structured progressions (ML = 15.5 progressions, DSRL = 14.8 progressions, p = 0.58), but the ML group performed the test procedure significantly faster (ML = 107 s, DSRL = 180 s, p < 0.001). The ML did not spend significantly longer time training (ML = 114 min, DSRL = 109 min, p = 0.84).
ML is a very efficient training form allowing novice trainees to learn how to perform a thorough, systematic, and quick flexible bronchoscopy. ML does not require longer time spent training compared to DSRL, and we therefore recommend training of future bronchoscopists by this method.
基于模拟的培训已被证明对学习可弯曲支气管镜检查有效。然而，尚无研究针对既定的熟练标准，即掌握学习（ML）来测试培训的效果。我们希望测试与定向自我调节学习（DSRL）相比，ML对新手支气管镜检查医师培训结束时表现的有效性。
在标准化模拟环境中，使用自动识别支气管节段的人工智能（AI）引导系统对无支气管镜检查经验的新手进行培训。他们被随机分为两组：ML组和DSRL组。ML组一直训练到完成两项达到熟练目标的操作：检查18个节段、18个结构化步骤、操作时间<120秒。DSRL组一直训练到他们不再认为训练有任何额外益处。然后两组在未启用AI引导的情况下进行结业测试。
共有24名参与者完成了研究，每组12人。两组的平均检查节段数（ML = 17.2个节段，DSRL = 17.3个节段，p = 0.85）和结构化步骤数（ML = 15.5个步骤，DSRL = 14.8个步骤，p = 0.58）都很高，但ML组进行测试操作的速度明显更快（ML = 107秒，DSRL = 180秒，p < 0.001）。ML组的训练时间没有显著更长（ML = 114分钟，DSRL = 109分钟，p = 0.84）。
ML是一种非常有效的培训形式，可让新手学员学习如何进行全面、系统且快速的可弯曲支气管镜检查。与DSRL相比，ML不需要更长的训练时间，因此我们建议用这种方法培训未来的支气管镜检查医师。
