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Record W2912287004 · doi:10.1155/2019/4569826

Systemic Sclerosis Pathogenesis and Emerging Therapies, beyond the Fibroblast

2019· review· en· W2912287004 on OpenAlex
Andrea Sierra-Sepúlveda, Alexia Esquinca-González, Sergio A. Benavides-Suárez, Diego E. Sordo-Lima, Adrián E. Caballero-Islas, Antonio R. Cabral-Castañeda, Tatiana Sofia Rodrı́guez-Reyna

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioMed Research International · 2019
Typereview
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPathogenesisDiseaseMedicineMultiple sclerosisMechanism (biology)ImmunologyFibrosisScleroderma (fungus)Immune systemBioinformaticsInflammationClinical trialAutoimmune diseasePathologyBiology

Abstract

fetched live from OpenAlex

Systemic sclerosis (SSc) is a complex rheumatologic autoimmune disease in which inflammation, fibrosis, and vasculopathy share several pathogenic pathways that lead to skin and internal organ damage. Recent findings regarding the participation and interaction of the innate and acquired immune system have led to a better understanding of the pathogenesis of the disease and to the identification of new therapeutic targets, many of which have been tested in preclinical and clinical trials with varying results. In this manuscript, we review the state of the art of the pathogenesis of this disease and discuss the main therapeutic targets related to each pathogenic mechanism that have been discovered so far.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.213
GPT teacher head0.422
Teacher spread0.208 · 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