Effect of Chemotherapy on Health-Related Quality of Life among Early-Stage Ovarian Cancer Survivors: A Study from the Population-Based Profiles Registry
Bibliographic record
Abstract
BACKGROUND: There is wide variation in the application of adjuvant chemotherapy in early-stage epithelial ovarian cancer. Our aim was to assess differences in health-related quality of life (hrqol) between patients with early-stage ovarian cancer who did or did not receive chemotherapy as adjuvant treatment. METHODS: = 191) were enrolled in this study. Patients were requested to complete questionnaires, including the cancer-specific (qlq-C30) and ovarian cancer-specific (qlq-OV28) quality of life measures from the European Organisation for Research and Treatment of Cancer. Primary outcome measures were the generic-and cancer-specific domain scores for hrqol in ovarian cancer survivors. RESULTS: Of the 107 patients (56%) who returned the questionnaires, 57 (53.3%) had received adjuvant chemotherapy and 50 (46.7%) had been treated with surgery alone. Significant differences in hrqol between those groups were found in the symptom scales for peripheral neuropathy, attitude toward sickness, and financial situation, with worse scores in the chemotherapy group. CONCLUSIONS: Results of our study show that patients who receive adjuvant chemotherapy have a significantly worse score for 3 aspects of hrqol. Efforts should be made to reduce use of adjuvant chemotherapy in early-stage ovarian cancer. Moreover, preventive strategies to improve long-term quality of life for those who need adjuvant chemotherapy should be 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".