Do common <i>in silico</i> tools predict the clinical consequences of amino‐acid substitutions in the CFTR gene?
Why this work is in the frame
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Bibliographic record
Abstract
Computational methods are used to predict the molecular consequences of amino-acid substitutions on the basis of evolutionary conservation or protein structure, but their utility in clinical diagnosis or prediction of disease outcome has not been well validated. We evaluated three popular computer programs, namely, PANTHER, SIFT and PolyPhen, by comparing the predicted clinical outcomes for a group of known CFTR missense mutations against the diagnosis of cystic fibrosis (CF) and clinical manifestations in cohorts of subjects with CF-disease and CFTR-related disorders carrying these mutations. Owing to poor specificity, none of tools reliably distinguished between individual mutations that confer CF disease from mutations found in subjects with a CFTR-related disorder or no disease. Prediction scores for CFTR mutations derived from PANTHER showed a significant overall statistical correlation with the spectrum of disease severity associated with mutations in the CFTR gene. In contrast, PolyPhen- and SIFT-derived scores only showed significant differences between CF-causing and non-CF variants. Current computational methods are not recommended for establishing or excluding a CF diagnosis, notably as a newborn screening strategy or in patients with equivocal test results.
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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 | 0.005 | 0.010 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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