POCUS in perioperative medicine: a North American perspective
Bibliographic record
Abstract
Ultrasound (US) performed at the point of care has found fertile ground in perioperative medicine. In the hands of anesthesiologists, transesophageal echocardiography (TEE) has become established as a powerful diagnostic and monitoring tool in the perioperative care of cardiac and non-cardiac patients. A number of point-of-care US (POCUS) applications are relevant to perioperative care, including airway, cardiac, lung and gastric US. Although guidelines exist to define the scope of practice for basic and advanced TEE, there remains a lack of such guidelines for perioperative point-of-care ultrasound (POCUS), despite a number of recent calls for action in the academic anesthesia community. POCUS training has been integrated into anesthesia residency curricula in Canada and the United States of America (USA). However, a nation-wide curriculum is still lacking. Many limitations to the development of perioperative POCUS curricula exist, including the need to define the scope of practice and design integrated longitudinal learning approaches. The main anesthesiologist societies in both the USA and Canada are promoting the development of guidelines and have introduced POCUS courses into their national conferences. Although bedside US imaging has been integrated into the curricula of many medical schools in North America, the need for specific national guidelines for the training and practice of POCUS in the perioperative setting by anesthesiologists is crucial to the further development of POCUS in perioperative 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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| 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".