Mapping EQ5D utilities from forced vital capacity and diffusing capacity in fibrotic interstitial lung disease
Bibliographic record
Abstract
OBJECTIVES: Fibrotic interstitial lung disease (ILD) includes a large group of conditions that lead to scarring of the lungs. The lack of available 5-level EuroQol 5D (EQ5D) data has limited the ability to conduct economic evaluations in ILD. The purpose of this study was to develop and validate a mapping algorithm that predicts EQ5D utilities from commonly collected pulmonary function measurements (forced vital capacity [FVC] and diffusing capacity of the lung for carbon monoxide [DLCO]) in fibrotic ILDs. METHODS: EQ5D utility and pulmonary function measurements from the Canadian Registry for Pulmonary Fibrosis were included. Ordinary least squares (OLS), beta regression, two-part, and tobit models were used to map EQ5D utilities from FVC or DLCO. Model performance was assessed by comparing the predicted and observed utilities. Subgroup analyses were also conducted to test how well models performed across different patient characteristics. The models were then externally validated in the Australian Idiopathic Pulmonary Fibrosis Registry. RESULTS: The OLS model performed as well as other more complex models (root mean squared error: 0.17 for FVC and 0.16 for DLCO). As with the other models, the OLS algorithm performed well across the different subgroups (except for EQ5D utilities < 0.5) and in the external validation cohort. CONCLUSION: We developed a mapping algorithm that predicts EQ5D utilities from FVC and DLCO, with the intent that this algorithm can be applied to clinical trial populations and real-world cohorts that have not prioritized collection of health-related utilities. The mapping algorithm can be used in future economic evaluations of potential ILD therapies.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".