Effect of Interventional Pulmonology Fellowships on Pulmonary Critical Care Fellows’ Core Bronchoscopy Competencies
Bibliographic record
Abstract
Abstract Background Currently, there is significant variability in bronchoscopy training across pulmonary and critical care medicine (PCCM) programs nationwide, including procedural volume and exposure to simulation training. Despite the increased number of interventional pulmonology (IP) fellowship programs in the United States, their direct educational impact on PCCM fellows’ bronchoscopy training is unknown. Objective To identify and quantify the differences in flexible bronchoscopy competency among PCCM fellows from institutes with IP fellowships compared with those without IP fellowships. Methods This multicenter, prospective cohort study included the assessment of PCCM fellows from two groups, using the Ontario Bronchoscopy Assessment Tool (OBAT): 1) PCCM fellowships with a coexistent IP fellowship program and 2) PCCM fellowships without an IP fellowship program. The primary outcome was the difference in mean score between the two groups; secondary outcomes included the mean OBAT score of first, second, and third-year (or above) fellows in the two groups and the percentage of fellows in the two groups who were capable of independently performing the procedure. Results There were five participating training sites: two with IP fellowships and three without IP fellowships. A total of 50 OBAT assessments were performed (25 in each group) by the supervising attending physician. The mean OBAT score was 3.58 ± 0.65 in the IP group compared with 4.33 ± 0.61 in the non-IP group (P < 0.001). The mean (standard deviation) OBAT scores of the first, second, and third-year (or above) fellows were 3.36 (0.5), 3.48 (0.4), and 4.53 (0.5) in the IP group and 3.75 (0.8), 4.25 (0.5), and 4.7 (0.3) in the non-IP group, respectively. The mean OBAT score was directly proportional to the number of procedures done by the fellows. Conclusion There was a statistically significant difference in the mean OBAT scores between the two groups; the mean OBAT score was higher in the non-IP fellowship group. Although a more comprehensive study is needed to fully account for the various factors that can impact bronchoscopy training, this study highlights a key difference in basic bronchoscopy training among PCCM trainees. The presence of IP fellowship is one of the many factors that can affect the basic bronchoscopy skills of PCCM fellows.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".