Towel Uterus Model for Uterine Compression Sutures Technical Skills Training: A Review of Literature and Development of a Performance Rubric
Bibliographic record
Abstract
Postpartum hemorrhage (PPH) continues to be the leading cause of maternal mortality worldwide, occurring in about five percent of deliveries. The most common cause of PPH is uterine atony, and a number of medical and surgical management techniques are available to prevent morbidity and mortality associated with PPH in this context. Uterine compression sutures provide a more conservative surgical approach, allowing for the preservation of fertility. Obstetrics and Gynecology (Ob/Gyn) residents need to be adequately trained to competently perform this technique. The goal of this surgical skills training is for Ob/Gyn residents to be able to surgically manage PPH using uterine compression sutures. A uterine towel model for surgical skills training in the use of uterine compression sutures was developed. The simulator is explained and compared to similar models. Possible ways to implement and use the simulator in a simulation curriculum are also described. A performance-based assessment rubric was also developed in order to formatively aid with the learning and understanding of the technique. Much work is still needed to test the validity and reliability of this tool, but based on current literature, results may be promising.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".