Reducing Uncertainty: Predictors of Stopping Chemotherapy Early and Shortened Survival Time in Platinum Resistant/Refractory Ovarian Cancer—The GCIG Symptom Benefit Study
Bibliographic record
Abstract
BACKGROUND: Clinicians and patients often overestimate the benefits of chemotherapy, and overall survival (OS), in platinum resistant/refractory ovarian cancer (PRROC). This study sought to determine aspects of health-related quality of life and clinicopathological characteristics before starting chemotherapy that were associated with stopping chemotherapy early, shortened survival, and death within 30 days of chemotherapy. MATERIALS AND METHODS: This study enrolled women with PRROC before starting palliative chemotherapy. Health-related quality of life was measured with EORTC QLQ-C30/QLQ-OV28. Chemotherapy stopped within 8 weeks of starting was defined as stopping early. Logistic regression was used to assess univariable and multivariable associations with stopping chemotherapy early and death within 30 days of chemotherapy; Cox proportional hazards regression was used to assess associations with progression-free and OS. RESULTS: < .012). CONCLUSION: Women with low GHS, RF, or PF before starting chemotherapy were more likely to stop chemotherapy early, with short OS. Self-ratings of GHS, RF, and PF could improve patient-clinician communication regarding prognosis and help decision-making in women considering chemotherapy for PRROC. IMPLICATIONS FOR PRACTICE: Measuring aspects of health-related quality of life when considering further chemotherapy in platinum resistant/refractory ovarian cancer (PRROC) could help identify women with a particularly poor prognosis who are unlikely to benefit from chemotherapy and could therefore be spared unnecessary treatment and toxicity in their last months of life. Self-ratings of global health status, role function, and physical function could improve patient-clinician communication regarding prognosis and help decision-making in women considering chemotherapy for PRROC.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".