Cystic Fibrosis–related Liver Disease
Bibliographic record
Abstract
OBJECTIVES: Hepatobiliary complications are a leading cause of morbidity and mortality in cystic fibrosis (CF) patients. Knowledge of the underlying pathological aspects and optimal clinical management is, however, sorely lacking. METHODS: We provide a summary of the lectures given by international speakers at the European Society for Paediatric Gastroenterology, Hepatology, and Nutrition (ESPGHAN) monothematic conference on cystic fibrosis-related liver disease (CFLD) held in Paris in January 2016, to discuss the status of our current knowledge of liver disease in CF patients, to define the critical areas that need to be addressed, and to resolve actions to elucidate relevant mechanisms of disease to optimise future therapeutic options. CONCLUSIONS: The need for a universal consensus on the definition of CFLD to clarify disease stage and to identify relevant biomarkers to assess disease severity was highlighted. A deeper understanding of the pathophysiology and prognostic factors for the long-term evolution of CFLD is fundamental to move forward and has a strong bearing on identifying potential treatments. Novel experimental models and new treatment options under investigation are discussed and offer hope for the near future of CFLD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".