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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".