Quality of life and understanding of disease status among cancer patients of different ethnic origin
Bibliographic record
Abstract
Patients managed in European or North American cancer centres have a variety of ethnic backgrounds and primary languages. To gain insight into the impact of ethnic origin, we have investigated understanding of disease status and quality of life (QoL) for 202 patients. Patients completed questionnaires in their first language (52 English, 50 Chinese, 50 Italian, 50 Spanish or Portuguese), including the Functional Assessment of Cancer Therapy - General (FACT-G) QoL instrument, questions about disease status, expectations of cure and the language and/or type of interpretation used at initial consultation. Physicians also evaluated their status of disease and expectation of cure, and performance status was estimated by a trained health professional. The initial consultation was usually provided in English (except for 32% of Chinese-speaking patients); interpretation was provided by a family member for 34% of patients with limited English proficiency (LEP) and by a bilingual member of staff for 21%. Patients underestimated their extent of disease and overestimated their probability of cure (P=0.001 and <0.0001, respectively). Estimates of probability of cure by the English speakers were closer to those of their physicians than the other groups (P=0.02). English-speaking patients reported better and Italian-speaking patients poorer overall QoL (P<0.001 for Italian vs other groups). Performance status was correlated with QoL and most closely related with the extent of disease. Understanding of cultural differences is important for optimal management of patients with cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".