ENDOSCOPIC ULTRASOUND EDUCATION IN THE USA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To maintain the high quality of endoscopic ultrasound (EUS) and EUS‐fine‐needle aspiration (EUS‐FNA) currently available in the United States and yet accommodate the increasing demand for EUS, the availability of and resources for EUS training must continue to increase. There are currently 28 formal EUS training centers in the US and one in Canada, which produce a total of approximately 30–35 expert endosonographers per year. In addition, there are an increasing number of endosonographers being trained through informal or non‐traditional methods. Training in EUS includes three aspects: cognitive (e.g. indications), pattern recognition (normal and pathologic EUS images), and manual dexterity (scope insertion, positioning, FNA). While all these aspects are readily addressed in a formal training setting, it becomes much more challenging in the informal setting. Case observation, but more importantly, hands‐on experience with patients is the rate‐limiting step for accelerated EUS training. Technology advancements (e.g. simulators, tele‐mentoring) may address some of these limitations. Collaborative efforts among different countries may also provide ‘win‐win’ solutions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it