EP31.18: Beat the clock: predicting surgical times for excision of endometriosis using preoperative ultrasound – a retrospective study
Bibliographic record
Abstract
The primary objective of this study is to determine the correlation between predicted surgical times using endometriosis ultrasound (US) with actual surgical times. Secondary objectives include determining the: 1) correlation between surgeon estimated surgical time and actual surgical time; 2) average anesthetic preparation times; and 3) predictive factors that may account for actual surgical time. This study was conducted at the Endometriosis Clinic at McMaster University in Hamilton, Canada. Patients were included if they underwent laparoscopic excision of endometriosis conducted by a single endometriosis surgeon and gynecologic sonologist between August 2020 and July 2022. Prediction of operating time was recorded using both 1) routine preoperative endometriosis ultrasound based on estimates of surgical time per disease site and 2) surgeon estimation by standard technique. These estimated times were compared with the actual OR time. Thirty-three patients were included. Mean (SD) US estimated OR time and surgeon estimated time was 113.9 (80.6) min and 184.4 (88.0) min respectively. The mean (SD) actual OR time was 172.4 (111.9) min. The average (SD) anesthetic preparation time was 20.3 (13.2) min. There was a strong significant correlation between US estimated OR time and actual OR time (r = 0.75, P-value<0.001). There was a strong significant correlation between surgeon estimated OR time and actual OR time (r = 0.74, P-value<0.001). Moreover, there was a strong and significant correlation between the number of disease sites detected during surgery and OR time (r= 0.69, P-value<0.001). The prediction of laparoscopic excision of endometriosis surgical time by endometriosis ultrasound is strongly correlated with actual surgical time. Though the number of disease sites at time of surgery was most strongly associated with actual OR time, further studies are needed to determine whether endometriosis disease extent by ultrasound staging can reliably predict OR time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".