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Record W2589573515 · doi:10.14740/jocmr2761w

Insulin-Like Growth Factor-1 Deficiency and Cirrhosis Establishment

2017· review· en· W2589573515 on OpenAlexvenueno aff
Rocío García de la Garza, Luis Alonso Morales-Garza, Irene Martín‐Estal, Inma Castilla‐Cortázar

Bibliographic record

VenueJournal of Clinical Medicine Research · 2017
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsCirrhosisMedicineContext (archaeology)FibrosisEtiologySteatosisMalnutritionImmunologyGastroenterologyInternal medicineBiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.584
GPT teacher head0.617
Teacher spread0.034 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations52
Published2017
Admission routes1
Has abstractyes

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