Comparison of diagnostic accuracy of transvaginal ultrasound with laparoscopy in the management of patients with adnexal masses
Bibliographic record
Abstract
PURPOSE: The aims of this study was (a) to compare the diagnostic accuracy of ultrasound examination with laparoscopic findings and both with the gold standard (histology) in the management of benign ovarian lesions, and (b) to assess the feasibility of laparoscopy in their diagnosis and management. METHODS: Prospective, comparative study (Canadian Task Force Classification II-2). A total of 117 women 15-59 years old were examined at outpatient department and had transvaginal ultrasound assessment. Ninety-eight women (three postmenopausal) with 105 cystic ovarian lesions met inclusion criteria and underwent operative laparoscopy. Histology was performed in all cases. RESULTS: Although laparoscopy showed an overall higher performance compared to transvaginal ultrasound, statistically significant difference was found only in the detection of endometriomas compared to ultrasound (P = 0.004 for sensitivity and P = 0.046 for specificity). CONCLUSION: Laparoscopy exhibits higher diagnostic accuracy, especially in endometriomas, compared to transvaginal scan. Laparoscopic diagnosis appears to be safe and accurate. Conservative laparoscopic management of benign adnexal masses is safe and with low morbidity.
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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 | 0.026 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".