Outcome of patients with borderline ovarian tumors: Results of the multicenter AGO ROBOT study.
Bibliographic record
Abstract
5005 Background: Borderline ovarian tumors (BOT) are a rare entity; current standard of care is based on the available data of predominantly small retrospective trials. Therefore we performed a pattern of care study including central pathology review. Methods: All consecutive patients diagnosed with BOT between 1998 and 2008 in 24 German institutions were included. Tumor samples were prospectively sent for central histopathological review to specialized gynecopathologists, clinical data were collected and patient follow-up was prospectively updated. Results: Pathological review was obtained in 1,042 of 1,236 pts resulting in 950 confirmed BOT cases analyzed here. Under- and overdiagnosis occurred in 3.8% and 5.0% of cases, respectively. Median age was 49 years; 84% of patients had FIGO stage I disease; serous type (S-BOT) was diagnosed in 64% and mucinous type (M-BOT) in 31%. Primary/re-staging surgery led to complete debulking in 92.3% of pts (residual disease 1.3%, unknown 6.4%). Adjuvant chemotherapy was given to 33 (3.5%) pts only. 165 (17%) underwent fertility preserving surgery and 31 (19%) of these patients had documented pregnancies thereafter. Overall, 74 (7.8%) pts experienced relapse and 43 (4.5%) died. Disease progression in the form of invasive carcinoma occurred in 30% of the relapses. Inadequate surgical staging, residual tumor, fertility sparing surgery and higher FIGO stage were associated with shorter progression-free survival (PFS). M-BOT showed a non-significant trend to longer PFS compared to S-BOT (p = 0.07). No differences were observed for laparatomy vs. laparoscopy as initial surgical approach or application of adjuvant chemotherapy. Conclusions: To this day, this is the largest data set available for BOT. Prognosis is favorable even without adjuvant therapy if correct surgical staging is performed. Both tumor characteristics and treatment variables had a significant impact on relapse rate and outcome. In contrast to previous studies, disease progression in the form of invasive carcinoma occurred in a significant amount of patients with relapsed disease.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".