Adverse events in the placebo arm of SOLO2/ENGOT-Ov21 maintenance trial of olaparib in recurrent ovarian cancer
Bibliographic record
Abstract
BACKGROUND: In women with platinum sensitive recurrent ovarian cancer (PSROC) undergoing maintenance treatment, adverse events (AEs) not attributable to the current treatment are not well understood. We used data from SOLO2/ENGOT-Ov21 to evaluate AEs reported in the placebo arm and to explore their longitudinal trajectories. METHODS: SOLO2/ENGOT-Ov21 (NCT01874353) randomly assigned 295 PSROC participants with a BRCA1/2 mutation to maintenance olaparib tablets (N = 196) or matching placebo (N = 99). For those assigned to placebo, we analyzed the AE (CTCAE v4.0) data including type, grade, time of onset and resolution, and attribution by investigator. RESULTS: Amongst 99 participants who received placebo 788 AEs were reported (95 % reporting ≥1 AE). Twenty-two percent of participants reported at least one grade ≥ 3 AE. Grade ≥ 2 AEs that persisted for over 100 days affected 21 % of participants. Recurring grade ≥ 1 AEs were experienced by 44 % of participants. Study investigators attributed 25 % of all AEs to the placebo treatment, with neutropenia (88 %), nausea (52 %) and thrombocytopenia (50 %) most attributed. Three percent of participants had a dose reduction, 19 % had treatment delays, and 2 % had permanent treatment discontinuation, due to AEs attributed to placebo. CONCLUSION: Virtually all PSROC participants in the SOLO2/ENGOT-Ov21 experienced one or more AE whilst on placebo. Furthermore, study investigators attributed one quarter of AEs to be related to placebo therapy and dose alterations and treatment changes were made based on these AE. Further work is needed to improve measurement and categorization of AEs in trials of maintenance therapy in PSROC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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".