Exploratory outcome analyses according to stage and/or residual disease in the ICON7 trial of carboplatin and paclitaxel with or without bevacizumab for newly diagnosed ovarian cancer
Bibliographic record
Abstract
OBJECTIVE: In the randomized phase 3 ICON7 trial (ISRCTN91273375), adding bevacizumab to chemotherapy for newly diagnosed ovarian cancer significantly improved progression-free survival (PFS; primary endpoint) but not overall survival (OS; secondary endpoint) in the intent-to-treat (ITT) population. We explored treatment effect according to stage and extent of residual disease. METHODS: Patients with stage IIB-IV or high-risk (grade 3/clear-cell) stage I-IIA ovarian cancer were randomized to receive six cycles of carboplatin and paclitaxel either alone or with bevacizumab 7.5 mg/kg every 3 weeks followed by single-agent bevacizumab for 12 further cycles (total duration 12 months). Post hoc exploratory analyses of subgroups defined by stage and extent of residual disease at diagnosis within the stage IIIB-IV population (European indication) was performed. RESULTS: The PFS benefit from bevacizumab was seen consistently in all subgroups explored. The PFS hazard ratio was 0.77 (95% confidence interval [CI], 0.59-0.99) in 411 patients with stage IIIB-IV ovarian cancer with no visible residuum and 0.81 (95% CI, 0.69-0.95) in 749 patients with stage IIIB-IV disease and visible residuum. As in the ITT population, no OS difference was detected in any subgroup except the previously described 'high-risk' subgroup. Safety results in analyzed subgroups were consistent with the overall population. CONCLUSIONS: Adding bevacizumab to front-line chemotherapy improves PFS irrespective of stage/residual disease. In patients with stage III with >1 cm residuum, stage IV or inoperable disease, this translates into an OS benefit. No OS benefit or detriment was seen in other subgroups explored.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".