SHIFTing goals in cystic fibrosis—managing extrapulmonary disease in the era of CFTR modulator therapy; Proceedings of the International Shaping Initiatives and Future Trends (SHIFT) Symposium
Bibliographic record
Abstract
Abstract Background Cystic fibrosis (CF) is a life‐shortening multisystem genetic disease. Although progressive pulmonary disease is the predominant cause of morbidity and mortality, improvements in treatment for CF‐related lung disease, with associated increase in longevity, have increased the prevalence of extrapulmonary manifestations 1 . Methods To discuss these issues, a multidisciplinary meeting of international leaders and experts in the field was convened in November 2021 at the Shaping Initiatives and Future Trends Symposium with the goal of highlighting shifting management paradigms in CF. The main topics covered were: (1) nutrition and obesity, (2) exocrine pancreas, (3) CF‐related diabetes, (4) CF liver disease, (5) CF‐related bone disease, and (6) post‐lung transplant care. This document summarizes the proceedings, highlighting the key priorities and important research questions that were discussed. Results Improved life expectancy, the advent of cystic fibrosis transmembrane conductance regulator modulators, and the increasing appreciation of the heterogeneity or spectrum of disease are leading to a shift in management for patients with cystic fibrosis. Care should be individualized to ensure that increased longevity is accompanied by improved extra‐pulmonary care and reduced morbidity.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".