Uterine Applications of the Laparoscopic Ultrasound
Bibliographic record
Abstract
Objectives To demonstrate the use and setup of a laparoscopic ultrasound (LUS), and show its application during a myomectomy. Design A surgical case and accompanying literature are reviewed to demonstrate the features of the LUS, the set-up of the LUS, and its use during a myomectomy. Subjects The patient included in this video gave consent for publication of the video and posting of the video online including social media, the journal website, scientific literature websites (such as PubMed, ScienceDirect, Scopus, etc.) and other applicable sites. Our patient was diagnosed with a multi-fibroid uterus, and had failed medical management. The surgery occurred in an elective outpatient operating room. Exposure A robotic myomectomy with the LUS is demonstrated, along with the use and set up of a LUS. Main Outcome Measure Number of fibroids resected at the time of myomectomy. Results LUS using a 5–10 MHz transducer delivered real-time, high-resolution imaging directly on uterine tissue, eliminating acoustic artifacts and overcoming reduced tactile feedback in minimally invasive surgery. As demonstrated in prospective literature, LUS detects a median of two additional fibroids per patient—predominantly FIGO Types 2 and 3—compared to preoperative imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".