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Record W4229922438 · doi:10.1038/labinvest.2012.36

Gynecologic & Obstetrics

2012· article· en· W4229922438 on OpenAlexaff
Bao‐Ping Zhu, X Lin, Stephen Rohan, Minghao Zhong, Rakesh K. Goyal, Elizabeth Gersbach, G Aggarwal, Anaís Malpica, Elizabeth D. Euscher, Cristina Alenda, Cecilia Egoavil, José Luís Soto, Adela Castillejo, Víctor Manuel Barberá, Mark Roman, A Sanchez, Jordi Navinés, Oscar Piñero, Carla Guarinós, Lucía Pérez–Carbonell, Michael Rodriguez, Gloria Peiró, Estefanía Rojas, Marcos González, Sonia Cigüenza, Juan Carlos Martínez Escoriza, Rodrigo Jover, F Aranda, X Al-Ibraheemi, Ran Duan, Ghassan Allo, Helen Mackay, Marjan Rouzbahman, Patricia Shaw, Marcus Q. Bernardini

Bibliographic record

VenueLaboratory Investigation · 2012
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsObstetricsMedicineGynecology

Abstract

fetched live from OpenAlex

and miR-1257 were signifi cantly overexpressed in sunitinib responders compared with non-responders and the fold change was 2.1, 2.3, 1.9, 1.9, 1.5 and 1.8, respectively (p values all <0.05). MiRNAs miR-9, miR-138, miR-9*, miR-376a*, miR-144* and miR-223* were signifi cantly down-regulated in sunitinib responders, and the fold change was 0.20, 0.23, 0.21, 0.53, 0.49 and 0.44, respectively (p values all <0.036). Conclusions: By whole genome miRNA screening, 12 miRNAs were found to have differential expression patterns between CCRCC that responded to sunitinib treatment and non-responders. Several of these miRNAs were reported in the literature to affect the maturation of immune regulatory bone marrow derived dendritic cells (miR-223) and hypoxia inducible pathways (miR-138). These fi ndings suggest that miRNAs could be informative biomarkers for predicting response to tyrosine kinase inhibitors in patients with mCCRCC.

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 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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.093
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.280
Teacher spread0.241 · 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 teacher head, 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

Citations1
Published2012
Admission routes1
Has abstractyes

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