American Society of Regional Anesthesia and Pain Medicine expert panel recommendations on point-of-care ultrasound education and training for regional anesthesiologists and pain physicians—part II: recommendations
Bibliographic record
Abstract
Point-of-care ultrasound (POCUS) is a critical skill for all regional anesthesiologists and pain physicians to help diagnose relevant complications related to routine practice and guide perioperative management. In an effort to inform the regional anesthesia and pain community as well as address a need for structured education and training, the American Society of Regional Anesthesia and Pain Medicine Society (ASRA) commissioned this narrative review to provide recommendations for POCUS. The recommendations were written by content and educational experts and were approved by the guidelines committee and the Board of Directors of the ASRA. In part II of this two-part series, learning goals and objectives were identified and outlined for achieving competency in the use of POCUS, specifically, airway ultrasound, lung ultrasound, gastric ultrasound, the focus assessment with sonography for trauma exam, and focused cardiac ultrasound, in the perioperative and chronic pain setting. It also discusses barriers to POCUS education and training and proposes a list of educational resources. For each POCUS section, learning goals and specific skills were presented in the Indication, Acquisition, Interpretation, and Medical decision-making framework.
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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".