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Record W2808742107 · doi:10.1093/annonc/mdy151.133

Antibodies matter: A meta-analysis of the prognostic value of human equilibrative nucleoside transporter 1 (hENT1) antibodies in pancreatobiliary cancer

2018· article· en· W2808742107 on OpenAlexaff
Dimas Yusuf, Larissa J. Vos, Arthur Lui, Zainab Abdelaziz, Sunita Ghosh, Jennifer L. Spratlin, John R. Mackey

Bibliographic record

VenueAnnals of Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsAlberta Cancer Foundation
Fundersnot available
KeywordsMedicineGemcitabineOncologyImmunohistochemistryInternal medicinePopulationAntibodyCancerPathologyImmunology

Abstract

fetched live from OpenAlex

Introduction: Gemcitabine, the primary drug for the treatment of pancreatobiliary cancer (PC), requires human equilibrative nucleoside transporter 1 (hENT1) to enter cells. High tumoral hENT1 expression has been linked to improved survival among PC patients treated with gemcitabine, however this finding has been inconsistent and studies used different anti-hENT1 immunohistochemistry (IHC) antibody assays.

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)0.015
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.036
Bibliometrics0.0040.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.181
GPT teacher head0.463
Teacher spread0.283 · 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 designMeta-analysis
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
Published2018
Admission routes1
Has abstractyes

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