Development and Validation of EPIC-USA: A COPD Policy Model for the United States
Bibliographic record
Abstract
Abstract RATIONALE Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality in the U.S. and poses a significant healthcare burden. Currently, there is no reference policy model of COPD in the US that tracks the lifetime disease trajectory and can be used for comparative evaluations of policies to reduce COPD burden across the life course. The Evaluation Platform in COPD (EPIC) is a validated, population-based whole disease model that was originally developed to be representative of the Canadian population ≥40 years. We adapted the EPIC model to the U.S. (EPIC-USA) and projected the future burden of COPD from 2015-2040. METHODS To adapt EPIC to the U.S., demographics, COPD prevalence, healthcare resource utilization and costs were updated or re-calibrated using US-based estimates from the literature. Components reflecting the epidemiology of COPD, such as lung function decline and rate of exacerbations are predicted in EPIC based on simulated patient characteristics (i.e. age, sex, smoking history, history of exacerbations) and were not updated since they were originally developed with data from U.S. cohorts. Healthcare costs and resource utilization were derived from the literature based on national private insurance claims data. Model calibration included benchmarking to validation targets by comparing simulated outcomes to U.S.-based data sources. COPD prevalence, defined as FEV1/FVC
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".