A novel lung disease phenotype adjusted for mortality attrition for cystic fibrosis Genetic modifier studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Genetic studies of lung disease in cystic fibrosis (CF) are hampered by the lack of a severity measure that accounts for chronic disease progression and mortality attrition. Further, combining analyses across studies requires common phenotypes that are robust to study design and patient ascertainment. Using data from the North American Cystic Fibrosis Modifier Consortium (Canadian Consortium for CF Genetic Studies, Johns Hopkins University CF Twin and Sibling Study, and University of North Carolina/Case Western Reserve University Gene Modifier Study), the authors calculated age-specific CF percentile values of FEV1 which were adjusted for CF age-specific mortality data. The phenotype was computed for 2,061 patients representing the Canadian CF population, 1,137 extreme phenotype patients in the UNC/Case Western study, and 1,323 patients from multiple CF sib families in the CF Twin and Sibling Study. Despite differences in ascertainment and median age, our phenotype score was distributed in all three samples in a manner consistent with ascertainment differences, reflecting the lung disease severity of each individual in the underlying population. The new phenotype score was highly correlated with the previously recommended complex phenotype, but the new phenotype is more robust for shorter follow-up and for extreme ages. A disease progression and mortality-adjusted phenotype reduces the need for stratification or additional covariates, increasing statistical power, and avoiding possible distortions. This approach will facilitate large-scale genetic and environmental epidemiological studies which will provide targeted therapeutic pathways for the clinical benefit of patients with CF.
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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 | 0.003 |
| 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 it