A Pilot Program Assessing Bronchoscopy Training and Program Initiation in a Low-income Country
Bibliographic record
Abstract
BACKGROUND: Flexible bronchoscopy is an essential procedure for the evaluation and management of the pulmonary disease. However, this technology and related training is not available in many low-middle income countries (LMICs). We conducted a pilot training program for flexible bronchoscopy in Uganda. METHODS: A multimodal curriculum was developed with pulmonologists from Uganda and the United States. The training included an online distance learning management system for video content, simulation, just-in-time training, and deliberate practice via clinical proctoring. Procedural standards and a de novo bronchoscopy suite were concurrently developed. Competency was assessed using the Bronchoscopic Skills and Tasks Assessment Tool written examination and the Ontario Bronchoscopy Assessment Tool. RESULTS: We trained 3 pulmonary physicians with no prior experience in flexible bronchoscopy. Three bronchoscopies with bronchoalveolar lavage were performed during the training and an additional 11 cases were performed posttraining. All 3 Ugandan physicians had an increase in their written Bronchoscopic Skills and Tasks Assessment Tool and Ontario Bronchoscopy Assessment Tool in the competent range (P<0.05). All bronchoscopies were successfully completed, adequate samples were obtained, and there were no procedure-related complications. CONCLUSION: Bronchoscopy implementation in LMICs is feasible, but requires competency-based training. Further studies are needed to validate this curriculum in LMICs, including the use of this type of curriculum for more complicated bronchoscopic procedures.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".