Endobronchial Ultrasound Knowledge, Implementation, and Perceived Barriers After Attendance at a Dedicated Hands-on Course
Bibliographic record
Abstract
Endobronchial ultrasound (EBUS) is a relatively new technology in the field of pulmonary medicine. To determine EBUS knowledge, current clinical utilization, and perceived barriers to EBUS implementation, we surveyed physicians who had previously attended a 2-day hands-on EBUS course in our center. Our survey response rate was 51%. Overall, we found that more than one-third of course participants were currently performing linear EBUS and that over half had access to EBUS for their patients through another physician in their center. EBUS knowledge was excellent and many physicians used EBUS in their current clinical setting in the diagnosis of sarcoidosis, mediastinal lymphadenopathy, and lung cancer. Reported barriers to EBUS implementation included the high cost of equipment (73%), high per procedure cost (23%), inadequate support staff (32%), and limitations regarding use of sedation and anesthesia (18%). Only 14% cited lack of adequate training as a barrier to EBUS implementation, and none believed that low patient volumes for EBUS was a barrier to its implementation. It seems that participation in an EBUS course is useful in helping physicians incorporate EBUS in their practice, but barriers remain, some of which may not be modifiable through such activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".