Mapping the <scp>EORTC QLQ‐C30</scp> and <scp>QLQ‐H</scp>&<scp>N35</scp>, onto <scp>EQ‐5D‐5L</scp> and <scp>HUI</scp>‐3 indices in patients with head and neck cancer
Bibliographic record
Abstract
Abstract Background We sought to develop mapping functions that use EORTC responses to approximate health utility (HU) scores for patients with head and neck cancer (HNC). Methods In total, 209 outpatients with HNC completed the EORTC QLQ‐C30 & QLQ‐H&N35 (EORTC), EQ‐5D‐5L and the HUI‐3. Results of the EORTC were mapped onto both EQ‐5D‐5L and HUI‐3 scores using ordinary least squares regression and two‐part models. Results The OLS model mapping EORTC onto the EQ‐5D‐5L performed best (adjusted R 2 = .75, 10‐fold cross‐validation RMSE = 0.064, MAE 0.050). The HUI‐3 model mapping onto EORTC through OLS was more limited (adjusted R 2 = .5746, 10‐fold cross cross‐validation RMSE = 0.168, MAE 0.080). The EQ‐5D‐5L model was able to discriminate between certain clinical indices of disease severity on subgroup analysis. Conclusion The EORTC to EQ‐5D‐5L mapping algorithm has good predictive validity and may enable researchers to translate EORTC scores into HU scores for head and neck 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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".