The American Society of Regional Anesthesia and Pain Medicine, the European Society of Regional Anaesthesia and Pain Therapy, and the Asian Australasian Federation of Pain Societies Joint Committee Recommendations for Education and Training in Ultrasound-Guided Interventional Pain Procedures
Bibliographic record
Abstract
The use of ultrasound in pain medicine for interventional axial, nonaxial, and musculoskeletal pain procedures is rapidly evolving and growing. Because of the lack of specialty-specific guidelines for ultrasonography in pain medicine, an international collaborative effort consisting of members of the Special Interest Group on Ultrasonography in Pain Medicine from the American Society of Regional Anesthesia and Pain Medicine, the European Society of Regional Anaesthesia and Pain Therapy, and the Asian Australasian Federation of Pain Societies developed the following recommendations for education and training in ultrasound-guided interventional pain procedures. The purpose of these recommendations is to define the required skills for performing ultrasound-guided pain procedures, the processes for appropriate education, and training and quality improvement. Training algorithms are outlined for practice- and fellowship-based pathways. The previously published American Society of Regional Anesthesia and Pain Medicine and European Society of Regional Anaesthesia and Pain Therapy education and teaching recommendations for ultrasound-guided regional anesthesia served as a foundation for the pain medicine recommendations. Although the decision to grant ultrasound privileges occurs at the institutional level, the committee recommends that the training guidelines outlined in this document serve as the foundation for educational training and the advancement of the practice of ultrasonography in pain medicine.
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".