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Record W4387812289 · doi:10.1016/j.annonc.2023.09.1542

40P Bioinformatic evaluation of the transcriptomic and immunologic profile of prostate tumors with high expression of kallikrein-2

2023· article· en· W4387812289 on OpenAlexfundno aff
Irene Moreno Candilejo, L. Paniagua Herranz, B. Doger de Spéville, Cristina Díaz‐Tejeiro, Cristina Nieto‐Jiménez, Adrián Sanvicente, J. Ramon, E. Calvo, A. Ocana Fernandez

Bibliographic record

VenueAnnals of Oncology · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsnot available
FundersChugai PharmaceuticalGenentechServierEuropean Society for Medical OncologyCRIS Cancer FoundationAstellas PharmaSierra OncologyIncyteRegeneron PharmaceuticalsEli Lilly and CompanyAstraZenecaAmerican Society of Clinical OncologyEisaiSociedad Española de Oncología MédicaBeiGeneDaiichi Sankyo EuropeSanofiAmgenPfizerPharmaMarCelgene
KeywordsCancer researchCD8MedicineProstate cancerCytotoxic T cellT cellPopulationImmune systemTranscriptomeCancerImmunologyBiologyInternal medicineGene expressionGeneIn vitroGenetics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.047
GPT teacher head0.313
Teacher spread0.266 · 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

Citations0
Published2023
Admission routes1
Has abstractno

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