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Record W3015056143 · doi:10.1016/j.ebiom.2020.102741

Corrigendum to ‘Roles of alternative RNA splicing of the Bif-1 gene by SRRM4 during the development of treatment-induced neuroendocrine prostate cancer’. [EBioMedicine 31 (2018) 267–275>

2020· erratum· en· W3015056143 on OpenAlexaff
Yu Gan, Yinan Li, Zhi Long, Ahn R. Lee, Ning Xie, Jessica M. Lovnicki, Yuxin Tang, Xiang Chen, Jiaoti Huang, Xuesen Dong

Bibliographic record

VenueEBioMedicine · 2020
Typeerratum
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProstate cancerCancer researchGeneRNA splicingAlternative splicingCancerAdenocarcinomaMedicineTranscriptomeProstateRNABiologyGene expressionExonInternal medicineGenetics

Abstract

fetched live from OpenAlex

The authors wish to point out that:i)There is a typo in one of the labels of Fig. 2a. In Fig. 2a (top right), “IHC Positive (H score < 0.3)” should be replaced with “IHC Negative (H score < 0.3)”;ii) These errors do not change the scientific conclusions of the article in any way. Roles of Alternative RNA Splicing of the Bif-1 Gene by SRRM4 During the Development of Treatment-induced Neuroendocrine Prostate CancerTreatment-induced neuroendocrine prostate cancer (t-NEPC) is an aggressive subtype of prostate cancer (PCa) that becomes more prevalent when hormonal therapy, chemotherapy, or radiation therapy is applied to patients with metastatic prostate adenocarcinoma (AdPC). How AdPC cells survive these anti-cancer therapies and progress into t-NEPC remains unclear. By comparing the whole transcriptomes between AdPC and t-NEPC, we identified Bif-1, an apoptosis-associated gene, which undergoes alternative RNA splicing in t-NEPC. Full-Text PDF Open Access

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)0.002
metaresearch head score (Gemma)0.015
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.122
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.1220.065

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 head0.037
GPT teacher head0.314
Teacher spread0.277 · 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
GenreEditorial

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

Citations0
Published2020
Admission routes1
Has abstractyes

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