Toxicities and Quality of Life during Cancer Treatment in Advanced Solid Tumors
Bibliographic record
Abstract
The purpose of the study was to identify subgroups of advanced cancer patients who experienced grade 3-4 toxicities as reported by their oncologists as well as identify the demographic, clinical, and treatment symptom characteristics as well as QoL outcomes associated with distinct profiles of each patient. A prospective, multicenter, observational study was conducted with advanced cancer patients of 15 different hospitals across Spain. After three months of systemic cancer treatment, participants completed questionnaires that evaluated psychological distress (BSI-18), quality of life (EORTC QLQ-C30) and fatigue (FAS). The most common tumor sites for the 557 cancer patients with a mean age of 65 years were bronchopulmonary, digestive, and pancreas. Overall, 19% of patients experienced high-grade toxicities (grade 3-4) during treatment. Patients with recurrent advanced cancer, with non-adenocarcinoma cancer, undergoing chemotherapy, and a showing deteriorated baseline status (ECOG > 1) were more likely to experience higher toxicity. Patients who experienced grade 3-4 toxicities during cancer treatment had their treatment suspended in 59% of the cases. Additionally, 87% of the patients had a dose adjustment or a cycle delayed in their treatment due to a high risk of dying during treatment. Future research should focus on identifying interventions to reduce high-grade toxicities and improve quality of life in cancer patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".