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Record W3163104866 · doi:10.1503/cmaj.78702

To act or not to act

2021· letter· en· W3163104866 on OpenAlexaffvenue
Geoffrey Lau, Renata Leong

Bibliographic record

VenueCanadian Medical Association Journal · 2021
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsSorafenibPsoriasisAngiogenesisMedicineVascular endothelial growth factorCancer researchHepatocellular carcinomaPlatelet-derived growth factor receptorGrowth factorImmunologyReceptorInternal medicineVEGF receptors

Abstract

fetched live from OpenAlex

Background: Psoriasis is a chronic, immune-mediated and angiogenesis-dependent disease. Activated keratinocytes in psoriatic lesions produce pro-angiogenic cytokines, including vascular endothelial growth factor (VEGF), which binds to vascular endothelial growth factor receptor (VEGFR) and promotes cell proliferation and angiogenesis. Sorafenib (BAY 43-9006) is a molecular multikinase inhibitor of RAF kinase, platelet-derived growth factor (PDGF), VEGFR-1, -2, -3, platelet-derived growth factor receptor (PDGFR)-β and c-Kit. This molecule inhibits tumor cell proliferation and angiogenesis and it is currently approved for the treatment of hepatocellular carcinoma (HCC). Case Report: We present the complete remission of resistant psoriasis in a hepatitis C virus (HCV)-infected cirrhotic patient who was treated with sorafenib, for recurrent HCC. Conclusion: Several targeted therapies have demonstrated efficacy against psoriasis. More research and well-designed studies, both in novel drugs and those already marketed for other indications, are needed to determine their value as potential novel therapies for psoriasis.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0300.018

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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2021
Admission routes2
Has abstractyes

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