Identifying Borderline Ovarian Tumor Recurrence Using Routine Ultrasound Follow-Up
Bibliographic record
Abstract
Borderline ovarian tumors (BOTs) are non-invasive tumors frequently diagnosed in young patients. Surgical removal of the uterus, fallopian tubes, ovaries, and omentum is considered definitive management, however fertility-sparing approach is a recognized option. Surveillance is important due to known recurrence, but there is controversy over the effectiveness of follow-up modalities. The objective is to determine the efficacy of ultrasound screening in identifying tumor recurrence. This retrospective chart review evaluated all patients consulted and/or treated surgically at our institution from January 2015 to June 2020 diagnosed with BOT. Patients were excluded if concurrently diagnosed with another gynecologic malignancy, did not have yearly ultrasound follow-up, or were lost to follow-up. This study included 56 patients, 17 of whom underwent fertility preserving surgery. The overall rate of recurrence was 10.7%; with recurrence rates of 23.5% for the fertility preserving surgery population and 5.1% for the definitive surgery population. Ultrasound first identified 5 of the 6 (83.3%) recurrences. Overall time to recurrence was 51.5 months. In conclusion, recurrences were identified on routine ultrasound screening prior to symptom onset or detection via physical exam in 83.3% of cases. While the best modality of follow-up remains controversial, this review provides evidence supporting the use of routine ultrasound follow-up for early detection of BOT recurrence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".