Standardizing education in interventional pulmonology in the midst of technological change
Bibliographic record
Abstract
Interventional pulmonology (IP) is a maturing subspecialty of pulmonary medicine. The robust innovation in technology demands standardization in IP training with both disease and technology driven training. Simulation based training should be considered a part of IP training as seen in other procedural and surgical subspecialties. Procedure volume is a component of training; however, this does not guarantee or translate into competency for learners. Basic competency skills can be assessed using standardized well validated assessment tools designed for various IP procedures including flexible bronchoscopy, endobronchial ultrasound guided transbronchial needle aspiration (EBUS TBNA), rigid bronchoscopy and chest tube placement; however, further work is needed to validate tools in all procedures as new technologies are introduced beyond fellowship training. Currently there are at least 39 IP fellowship programs in the United States (US) and Canada which has led to improved training by accreditation of programs who meet rigorous requirements of standardized curriculum and procedural volume. The challenge is to be innovative in how we teach globally with intention and how to best integrate new evolving technology training for those not only during fellowship training but also beyond fellowship training.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".