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Record W2777827957 · doi:10.1111/cid.12569

Identifying a combined biomarker for bisphosphonate‐related osteonecrosis of the jaw

2017· article· en· W2777827957 on OpenAlexvenueno aff
Ki‐Yeol Kim, Xianglan Zhang, In–Ho Cha

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2017
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsnot available
FundersNational Research Foundation of Korea
KeywordsOsteonecrosis of the jawMedicineBisphosphonateBiomarkerMicroarray analysis techniquesBisphosphonate-associated osteonecrosis of the jawBioinformaticsGeneGene expressionOncologyOsteoporosisInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: For this study, the aim was to identify combined biomarkers associated with bisphosphonate-related osteonecrosis of the jaw (BRONJ). MATERIALS AND METHODS: Microarray data for GSE7116 were downloaded from the Gene Expression Omnibus database, which contains 26 samples, including without ONJ, and 5 healthy volunteers. The combined biomarkers were identified using principal component analysis, and the pathway enrichment analyses were performed using the DAVID online tool. RESULTS: Two hundred differently expressed genes between groups were detected according to the significances. From functional annotation, Y-box binding protein 1 and heterogeneous nuclear ribonucleoprotein C were found to be included in the most significant 10 pathways. Ten combined gene sets were identified that were effective in classifying multiple myeloma (MM) with ONJ and MM without ONJ. CONCLUSION: Identifying combined gene expression profiles is expected to contribute to more personalized management of BRONJ and to improve existing therapies, and it will be helpful in finding new therapies by identifying more predictive biomarkers.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.263
GPT teacher head0.528
Teacher spread0.265 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2017
Admission routes1
Has abstractyes

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