Tumor Treating Fields therapy in platinum-resistant ovarian cancer: Results of the ENGOT-ov50/GOG-3029/INNOVATE-3 pivotal phase 3 randomized study
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Tumor Treating Fields (TTFields) are electric fields that disrupt processes critical for cancer cell viability and tumor progression. The pivotal, phase 3 ENGOT-ov50/GOG-3029/INNOVATE-3 study evaluated efficacy and safety of TTFields therapy with paclitaxel (PTX) vs PTX in patients with platinum-resistant ovarian cancer (PROC). PATIENTS AND METHODS: weekly) or PTX. Primary endpoint was overall survival (OS). Exploratory post-hoc analyses assessed OS in pegylated liposomal doxorubicin (PLD)-naive patients. RESULTS: Between March 2019 and November 2021, 558 patients (ECOG PS 0, 60.2 %; median [range] age, 62 [22-91] years) were assigned TTFields+PTX (n = 280) or PTX (n = 278). 24.4 % had 4 + prior LOT. Median OS was 12.2 months with TTFields+PTX vs 11.9 months with PTX (HR, 1.01; 95 % CI, 0.83-1.24; p = 0.89). Grade ≥ 3 adverse events (AEs) were similar between treatment groups. Grade 1/2 device-related skin AEs occurred in 83.6 % of patients receiving TTFields therapy. In exploratory post-hoc analysis in PLD-naive patients, median OS was 16 months with TTFields+PTX (n = 113) vs 11.7 months with PTX (n = 88; nominal HR, 0.67; 95 % CI, 0.49-0.94; p = 0.03). CONCLUSIONS: No new safety signals were identified. TTFields+PTX did not significantly improve OS compared with PTX in the intent-to-treat population. An exploratory post-hoc analysis suggests a potentially favorable benefit-risk profile for TTFields therapy in PLD-naive patients.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it