Closing the knowledge gap in pelvic neuroanatomy: assessment of a cadaveric training program
Bibliographic record
Abstract
BACKGROUND: The objective of this study is to characterize participants in a laparoscopic cadaveric neuroanatomy course and assess knowledge of pelvic neuroanatomy before and after this course. METHODS: This is a survey-based cohort study with a setting in a university educational facility. The participants are surgeons in a multiday laparoscopic cadaveric pelvic neuroanatomy course. Participants completed a precourse survey, including demographics and comfort with laparoscopic surgery. They then completed an identical precourse and postcourse anatomic knowledge test. Main outcomes are scores on the anatomic knowledge test precourse and postcourse. RESULTS: 44 respondents were included: 25 completed fellowship, 15 completed residency, 2 were residents, and 2 were fellows. Participants were on average 11.09 years post training, with an average of 8.67 years from training if they completed fellowship and 18.62 years if they completed residency only. 22 of 42 respondents strongly agreed or agreed they are comfortable performing complex laparoscopic hysterectomies. The average precourse score was 32.18/50 points and the mean difference score (MDS, defined as mean of Postcourse scores minus Precourse scores) was 9.80, showing significant improvement (p < 0.001). Precourse and MDS scores were not significantly different when comparing country of practice, level of training, or time since training. CONCLUSION: Baseline knowledge of pelvic neuroanatomy was similar among groups when comparing fellowship status, place of training, or time since training. There was significant improvement in knowledge after training in this dissection method. This course garnered interest from surgeons with broad training backgrounds.
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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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".