Experiences of radiotherapy for treatment of Granulosa Cell Tumor of the ovary: insights from the GCT-survivor sisters
Bibliographic record
Abstract
BACKGROUND: Optimal treatment of ovarian Granulosa Cell Tumors (GCT) is uncertain due to a lack of evidence from randomized trials. The role of radiotherapy is unclear and recommendations differ between countries. Through an ongoing collaboration with the multinational closed GCT Survivor Sisters (GCT-SS) Facebook™ group (membership n = 1800) we developed a survey to understand members' experiences of radiotherapy. METHODS: GCT-SS members (≥18+ years) were invited to complete the survey assessing i) diagnosis and treatment, ii) radiotherapy experiences and iii) radiotherapy impact. Two questions allowed free-text responses about radiotherapy experiences. Country of residence was assessed (USA, Canada, United Kingdom (UK), European Union (EU), Australia/New Zealand (Aus/NZ); Other). Logistic regression examined factors associated with radiotherapy. RESULT: Surveys from 1017 members (96 % Adult-GCT) are analysed. Respondents were mostly from the USA (62 %), diagnosed post-2015 (67 %), and 44 % had recurrent disease. Overall, 12 % of respondents reported radiotherapy (9 % received, 3 % planned), mostly for recurrent (23 %) disease. In univariate analyses, radiotherapy for recurrent disease differed by country (p = .019) (Canada (12 %), UK (14 %), EU (14 %), Aus/NZ (36 %), USA (25 %)); age (p = .02) (≥50 (26 %) ≤50 (17 %)) having chemotherapy (p = .019) and hormonal therapy (p < .001). Analyses of open-ended questions found five themes: four positive (it worked; easier than other treatments, gave me back my life, gives hope) and one negative reflecting severe side-effects. CONCLUSIONS: Radiotherapy for GCT is not common and reflects treatment recommendations in different countries. As women mostly report positive experiences, studies are needed to develop an evidence-base regarding its optimal timing and sequencing in managing GCT.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".