Obstetric and Gynecologic Ultrasound Curriculum and Competency Assessment in Residency Training Programs: Consensus Report
Bibliographic record
Abstract
Ultrasound imaging has become integral to the practice of obstetrics and gynecology. With increasing educational demands and limited hours in residency programs, dedicated time for training and achieving competency in ultrasound has diminished substantially. The American Institute of Ultrasound in Medicine assembled a multisociety task force to develop a consensus-based, standardized curriculum and competency assessment tools for obstetric and gynecologic ultrasound training in residency programs. The curriculum and competency assessment tools were developed based on existing national and international guidelines for the performance of obstetric and gynecologic ultrasound examinations and thus are intended to represent the minimum requirement for such training. By expert consensus, the curriculum was developed for each year of training, criteria for each competency assessment image were generated, the pass score was established at, or close to, 75% for each, and obtaining a set of 5 ultrasound images with pass score in each was deemed necessary for attaining each competency. Given the current lack of substantial data on competency assessment in ultrasound training, the task force expects that the criteria set forth in this document will evolve with time. The task force also encourages use of ultrasound simulation in residency training and expects that simulation will play a significant part in the curriculum and the competency assessment process. Incorporating this training curriculum and the competency assessment tools may promote consistency in training and competency assessment, thus enhancing the performance and diagnostic accuracy of ultrasound examination in obstetrics and gynecology.
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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 | 0.110 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".