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Record W2155325899 · doi:10.3390/ijms14036516

Correction: Liang, J., et al. Antisense Oligonucleotide Against Clusterin Regulates Human Hepatocellular Carcinoma Invasion Through Transcriptional Regulation of Matrix Metalloproteinase-2 and E-Cadherin. Int. J. Mol. Sci. 2012, 13, 10594-10607

2013· erratum· en· W2155325899 on OpenAlexaboutno aff
Dong Chen, Yan Wang, Kejun Zhang, Xuelong Jiao, Bomin Yan, Jun Liang

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2013
Typeerratum
Languageen
FieldMedicine
TopicClusterin in disease pathology
Canadian institutionsnot available
Fundersnot available
KeywordsHepatocellular carcinomaBiologyCancer research

Abstract

fetched live from OpenAlex

The original version of the paper reports that "OGX-011 is a second generation 21-mer oligonucleotide with a 20-O-(2-methoxy)-ethyl modification, generously provided by OncoGenex Technologies (OncoGenex, Vancouver, Canada)" [1] (p. 10602). OGX-011 was not provided by OncoGenex Technologies directly. Therefore, we would like to correct the wording to: "OGX-011 was obtained without the benefit of an agreement with OncoGenex, or The University British Columbia, or any other party". The authors would like to apologize for any inconvenience this may have caused to the readers of this journal.

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)0.004
metaresearch head score (Gemma)0.042
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 score0.029
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.042
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0040.002
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0290.018

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 head0.026
GPT teacher head0.307
Teacher spread0.281 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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Same venueInternational Journal of Molecular SciencesSame topicClusterin in disease pathologyFrench-language works237,207