International Musculoskeletal Radiology Conference and Ultrasound Workshop: Lessons Learned From an Educational Outreach Event in Jakarta, Indonesia
Bibliographic record
Abstract
ABSTRACT Purpose Radiology Across Borders (RAB) organizes volunteer educational visits to developing nations. While Indonesia has general radiology programs, it lacks subspecialty training—including musculoskeletal (MSK) imaging. Our goal was to evaluate the effectiveness of an ultrasound (US) workshop and lecture series in improving local clinician comfort with MSK imaging, while aiming to guide others in similar future projects. Methods In November 2023, an educational conference occurred over 2 days in Jakarta, Indonesia, consisting of a half‐day MSK US hands‐on workshop and a lecture series delivered in didactic and interactive formats. Pre‐ and post‐US workshop surveys, as well as a post‐conference survey, were distributed. Using a 5‐point Likert scale, means and standard deviations were calculated. An independent t‐test evaluated pre‐ and post‐US workshop comfort levels with MSK imaging. Common themes were identified from open‐ended feedback using a thematic analysis framework. Results For the US workshop, 11 and 13 participants responded to the pre‐ and post‐surveys, respectively. There was a significant improvement in MSK US imaging comfort level (t (22) = −2.34, p = 0.0145). The main suggestion was additional time devoted to individual hands‐on scanning practice. For the post‐conference feedback survey, 46 participants responded. The feedback was overall positive, with all questions receiving a mean score > 4. Specifically, the interactive “Interesting Cases Discussion” sessions were highly regarded. Conclusion Overall, participant comfort with MSK imaging improved. Detailed coordination and bidirectional relationships made the conference possible. This manuscript highlights positive tactics and areas for improvement.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".