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Record W2315054420 · doi:10.3109/03008207.2016.1168409

The first international workshop on the epigenetics of osteoarthritis

2016· article· en· W2315054420 on OpenAlexaff
Ingrid Meulenbelt, Nidhi Bhutani, Wouter den Hollander, Steffen Gay, Udo Oppermann, Louise N. Reynard, Andrew Skelton, David A. Young, Frank Beier, John Loughlin

Bibliographic record

VenueConnective Tissue Research · 2016
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsWestern University
FundersMedical Research CouncilVersus ArthritisOsteoarthritis Research Society International
KeywordsEpigeneticsDNA methylationHistoneDiseaseOsteoarthritisBioinformaticsComputational biologyMedicineBiologyNeuroscienceGeneticsDNAGeneAlternative medicinePathologyGene expression

Abstract

fetched live from OpenAlex

Osteoarthritis (OA) is a major clinical problem across the world, in part due to the lack of disease-modifying drugs resulting, to a significant degree, from our incomplete understanding of the underlying molecular mechanisms of the disease. Emerging evidence points to a role of epigenetics in the pathogenesis of OA, but research in this area is still in its early stages. In order to summarize current knowledge and to facilitate the potential coordination of future research activities, the first international workshop on the epigenetics of OA was held in Amsterdam in October 2015. Recent findings on DNA methylation and hydroxymethylation, histone modifications, noncoding RNAs, and other epigenetic mechanisms were presented and discussed. The workshop demonstrated the advantage of bringing together those working in this nascent field and highlights from the event are summarized in this report in the form of summaries from invited speakers and organizers.

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)0.007
metaresearch head score (Gemma)0.006
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: Other · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0020.007
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0270.008

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 head0.076
GPT teacher head0.375
Teacher spread0.299 · 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
GenreOther

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

Citations8
Published2016
Admission routes1
Has abstractyes

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