Outcome according to residual disease (surgeon's report vs pre‐chemotherapy imaging) in patients with bevacizumab‐treated ovarian cancer: Analysis of the ROSiA study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The single-arm ROSiA study evaluated frontline bevacizumab for advanced ovarian cancer. We explored how discordant surgically and radiologically assessed postoperative residual disease affects outcomes. METHODS: After debulking surgery, 1021 patients received 4 to 8 cycles of carboplatin-paclitaxel plus bevacizumab until progression or up to 24 months. The primary endpoint was safety; progression-free survival (PFS) was a secondary endpoint. We performed post hoc exploratory PFS analyses in four subgroups: surgeon-reported no visible residuum (NVR) without target lesions; surgeon-reported NVR with target lesions; macroscopic (≤1 cm) residuum; and >1 cm residuum. RESULTS: Surgical and radiological assessments were concordant in 94% of patients; 61 patients (6%; 21% of those with surgeon-reported NVR) had NVR with target lesions. Median PFS was numerically longest in patients with concordant surgically/radiologically assessed NVR (35.5 months), intermediate for surgeon-reported NVR with target lesions (31.8 months), and shortest for visible residuum (27.9 and 20.2 months for visible residuum ≤1 and >1 cm, respectively). One-year and 2-year PFS rates showed the same pattern. CONCLUSIONS: These analyses suggest that prognosis is potentially worse in patients with radiologically detected target lesions despite surgeon-reported NVR compared with concordant NVR by both assessment methods. Postsurgical imaging may add valuable prognostic information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".