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Record W2137218317 · doi:10.1186/1477-7827-1-71

Mitogen-activated protein kinases in normal and (pre)neoplastic ovarian surface epithelium

2003· review· en· W2137218317 on OpenAlexafffund
Kyung‐Chul Choi, Nelly Auersperg, Peter C. K. Leung

Bibliographic record

VenueReproductive Biology and Endocrinology · 2003
Typereview
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsBC Children's HospitalChildren's & Women's Health Centre of British ColumbiaUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsMAPK/ERK pathwayKinaseSignal transductionMitogen-activated protein kinaseCell biologyProtein kinase ABiologyCancer researchInternal medicineMedicine

Abstract

fetched live from OpenAlex

Mitogen-activated protein kinases (MAPKs) are a group of serine/threonine kinases which are activated in response to a diverse array of extracellular stimuli and mediate signal transduction from the cell surface to the nucleus. It has been demonstrated that MAPKs are activated by external stimuli including chemotherapeutic agents, growth factors and reproductive hormones in ovarian surface epithelial cells. Thus, the MAPK signaling pathway may play an important role in the regulation of proliferation, survival and apoptosis in response to these external stimuli in ovarian cancer. In this article, an activation of the MAPK signaling cascade by several key reproductive hormones and growth factors in epithelial ovarian cancer is reviewed.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.034
GPT teacher head0.334
Teacher spread0.300 · 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
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

Citations39
Published2003
Admission routes2
Has abstractyes

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