A mapping algorithm of health preferences from EORTC QLQ C30 to health utility index mark 3 (HUI3) in advanced colorectal cancer.
Bibliographic record
Abstract
547 Background: The National Cancer Institute of Canada CO17 study, which showed that patients with advanced colorectal cancer had improved overall survival and derived health related quality of life benefits (measured with EORTC QLQ C30) when treated with cetuximab, collected health preferences with HUI3 prospectively. We examined the relationship between baseline health utilities and quality of life, and constructed a mapping algorithm to derive health utilities from EORTC QLQ C30. Methods: Data from 545 patients including baseline characteristics (age, gender, treatment arm, K-ras, ECOG PS, etc.), health preferences (HUI3), EORTC QLQ C30 five function scales, a two-item global health status (GHS) scale, three symptom scales, and six single items were obtained from the CO17 dataset. Correlations among HUI3 and EORTC QLQ C30 scales and baseline characteristics were examined. Multivariable linear regression model was constructed to develop a mapping algorithm to derive HUI3 from EORTC QLQ C30 scales and/or baseline characteristics. Leave-one-out cross validation (LOOCV) mean absolute error (MAE) and root mean square error (RMSE) were calculated to examine predictive ability. Results: The mean HUI3 was 0.717 (SD = 0.235). HUI3 was significantly correlated with baseline ECOG PS, number of disease sites and the presence of liver metastasis, but not with age, gender, treatment arms or K-ras. HUI3 was also significantly correlated with all EORTC QLQ C30 scales except the diarrhea scale. Multivariable regression showed that HUI3 remained significantly associated with four of the five functional scales (physical, role, cognitive and emotional), the pain scale and the GHS scale. A mapping algorithm consisting of these 6 scales resulted in a model with an adjusted R2 of 0.61, and LOOCV mean error of -0.00014, MAE of 0.11, and RMSE of 0.15. Conclusions: Health preferences as measured by HUI3 are significantly associated with HRQL as measured by EORTC QLQ C30 in patients with advanced refractory colorectal cancer. Our mapping will allow for the generation of health preference values in advanced colorectal cancer when only EORTC QLQ C30 results exist in order to conduct cost-effectiveness analysis.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".