Tumor Treating Fields therapy in platinum-resistant ovarian cancer: Results of the ENGOT-ov50/GOG-3029/INNOVATE-3 pivotal phase 3 randomized study
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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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".