Perspectives on the translation of in-vitro studies to precision medicine in Cystic Fibrosis
Bibliographic record
Abstract
Recent strides towards precision medicine in Cystic Fibrosis (CF) have been made possible by patient-derived in-vitro assays with the potential to predict clinical response to small molecule-based therapies. Here, we discuss the status of primary and stem-cell derived tissues used to evaluate the preclinical efficacy of CFTR modulators highlighting both their potential and limitations. Validation of these assays requires correlation of in-vitro responses to in-vivo measures of clinical biomarkers of disease outcomes. While initial efforts have shown some success, this translation requires methodologies that are sensitive enough to capture treatment responses in a CF population that now predominantly has mild lung disease. Future development of in-vitro and in-vivo biomarkers will facilitate the generation of new therapeutics particularly for those patients with rare mutations where clinical trials are not feasible so that in the future every CF patient will have access to effective targeted therapies.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".