Ureter Visualization With Transvaginal Ultrasound
Bibliographic record
Abstract
OBJECTIVES: To evaluate the learning curve of gynecologic surgical fellows (ie, in training) to properly identify the ureters in real time while simultaneously performing and interpreting transvaginal ultrasound (TVUS) examinations. METHODS: We performed a prospective study, which took place at 2 centers in Sydney, Australia, from December 2017 to December 2018. Three fellows (F1-F3), of varying prestudy ultrasound (US) experience, were recruited to participate. One hundred fifty predetermined examinations were planned. A TVUS examination was performed by the study reference standard (an expert in gynecologic US). Subsequently, the fellows performed a focused component to identify bilateral ureters, having been blinded to the patient's clinical history and reference standard findings. Immediate feedback and hands-on teaching were provided after each of the fellow's evaluations were complete. To evaluate the number of scans needed to gain competency, the cumulative summation test for the learning curve was used. RESULTS: A total of 150 examinations were performed on 145 patients. One patient had a single ureter, and 1 patient had US evidence of hydroureter. The cumulative summation test for the learning curve for bilateral ureter identification showed that F1 did not reach competency by 50 TVUS examinations, whereas F2 and F3 required 41 and 31 TVUS examinations to reach competency, respectively. CONCLUSIONS: Contrary to other studies on the topic, this study suggests that although it is feasible for surgical fellows to learn TVUS identification of bilateral ureters, not all fellows can reach competency during a program based on a predefined number of scans. We advocate for an individualized, competency-based medical education model in learning US for identifying the ureters.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".