Standardization of Laparoscopic Pelvic Examination: A Proposal of a Novel System
Bibliographic record
Abstract
Objective. Laparoscopic pelvic assessment is often performed in a nonstandardized fashion depending on the surgeon's discretion. Reporting anatomic findings is inconsistent and lesions in atypical locations may be missed. We propose a method for systematic pelvic assessment based on anatomical landmarks. Design. Retrospective analysis. Setting. Tertiary care academic medical center. Intervention. We applied this system to operative reports of 540 patients who underwent diagnostic or operative laparoscopy for unexplained infertility between 2006 and 2012. The pelvis was divided into 2 midline zones (zone I and II) and right and left lateral zones (zone III and IV). All reports were evaluated for the comprehensiveness of description with respect to normal findings or pathology for each zone. Results. Of 540 surgeries, all reports commented on the uterus, tubes, and ovaries (100%), but only 17% (n = 93, 95% CI: 13.8-20.2) commented on the dome of the bladder and the anterior cul-de-sac. 24% (n = 130, 95% CI: 20.4-27.6) commented on the posterior cul-de-sac, and 5% (n = 29, 95% CI: 3.2-6.8) commented on the pelvic sidewall. Overall, 6% (n = 34, 95% CI: 4-8) reported near complete documentation of the pelvic zones. Conclusion. Implementation of a systematic approach for laparoscopic pelvic examination will enhance the diagnostic accuracy and provide better communication between care providers. In the absence of pelvic pathology, we recommend a minimum of 6 photographs of the 6 pelvic zones.
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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 | 0.054 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".