Insulin-Like Growth Factor-1 Deficiency and Cirrhosis Establishment
Bibliographic record
Abstract
Cirrhosis represents the final stage of chronic liver damage, which can be due to different factors such as alcohol, metabolic syndrome with liver steatosis, autoimmune diseases, drugs, toxins, and viral infection, among others. Nowadays, cirrhosis is an important health problem and it is an increasing cause of morbidity and mortality, being the 14th most common cause of death worldwide. The physiopathological pathways that lead to fibrosis and finally cirrhosis partly depend on the etiology. Nevertheless, some common features are shared in this complex mechanism. Recently, it has been demonstrated that cirrhosis is a dynamic process that can be altered in order to delay or revert fibrosis. In addition, when cirrhosis has been established, insulin-like growth factor-1 (IGF-1) deficiency or reduced availability is a common condition, independently of the etiology of chronic liver damage that leads to cirrhosis. IGF-1 deprivation seriously contributes to the progressive malnutrition of cirrhotic patient, increasing the vulnerability of the liver to establish an inflammatory and oxidative microenvironment with mitochondrial dysfunction. In this context, IGF-1 deficiency in cirrhotic patients can justify some of the common characteristics of these individuals. Several studies in animals and humans have been done in order to test the replacement of IGF-1 as a possible therapeutic option, with promising results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".