Determining the Relationship Between Toxicity and Quality of Life in an Ovarian Cancer Chemotherapy Clinical Trial
Bibliographic record
Abstract
PURPOSE: This analysis of data from a randomized trial of chemotherapy in epithelial ovarian cancer sought to determine whether a relationship exists between the presence and severity of the most commonly observed toxic effects and the corresponding quality of life (QOL) items. PATIENTS AND METHODS: One hundred fifty-two eligible patients accrued from Canada by the National Cancer Institute of Canada Clinical Trials Group on a randomized trial of paclitaxel and cisplatin versus cyclophosphamide/cisplatin were included in the analysis. Toxicity to the chemotherapeutic treatments was subjectively evaluated using a trial-specific checklist for ovarian cancer and the European Organization for Research and Treatment of Cancer QLQ C30+3 questionnaire. Assessments were conducted at baseline, before each cycle of treatment (3 weeks), and at each 3-month follow-up during the next 2 years (or until progression). RESULTS: The most frequently observed symptoms experienced during or shortly following chemotherapy were neurosensory loss, lethargy, nausea, vomiting, and alopecia. Regression analyses revealed that change scores of QOL items related to motor weakness and gastrointestinal pain were common predictors for the change global QOL score during protocol treatment; and change scores of QOL items related to lethargy or fatigue and change toxicity grade of mood predicted the change global QOL score after patients were off treatment. CONCLUSION: The use of the European Organization for Research and Treatment of Cancer QLQ C30+3 and trial-specific checklist was able to assess the effect of expected toxicities on patient' s QOL during and following treatment, and so may be useful in addressing the concerns regarding methodological issues that have limited the acquisition of prospective, longitudinal treatment-related toxicity data.
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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 | 0.067 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".