MétaCan
Menu
Back to cohort
Record W1898194260 · doi:10.1016/j.ccell.2015.09.003

Targeting Human Cancer by a Glycosaminoglycan Binding Malaria Protein

2015· article· en· W1898194260 on OpenAlexafffund
Ali Salanti, Thomas Mandel Clausen, Mette Ø. Agerbæk, Nader Al-Nakouzi, Madeleine Dahlbäck, Htoo Zarni Oo, Sherry Lee, Tobias Gustavsson, Jamie R. Rich, Bradley J. Hedberg, Yang Mao, Line Barington, Marina Ayres Pereira, Janine LoBello, Makoto Endo, Ladan Fazli, Jo Soden, Chris Kedong Wang, Adam F. Sander, Robert Dagil, Susan Thrane, Peter Johannes Holst, Le Meng, Francesco Favero, Glen J. Weiss, Morten A. Nielsen, Jim Freeth, Torsten O. Nielsen, Joseph Zaia, Nhan L. Tran, Jeff Trent, John S. Babcook, Thor G. Theander, Poul H. Sorensen, Mads Daugaard

Bibliographic record

VenueCancer Cell · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicInvertebrate Immune Response Mechanisms
Canadian institutionsCentre for Drug Research and DevelopmentVancouver Hospital and Health Sciences CentreUniversity of British Columbia
FundersEuropean Research CouncilStand Up To CancerSpar Nord FondenNovo Nordisk FondenSwedish Foundation for International Cooperation in Research and Higher EducationHarboefondenNational Institute of General Medical SciencesAugustinus FondenNovo Nordisk UK Research FoundationProstate Cancer CanadaAmerican Association for Cancer ResearchSt. Baldrick's FoundationKræftens BekæmpelseInnovationsfondenU.S. Department of DefenseNational Cancer InstituteEntertainment Industry Foundation
KeywordsPlasmodium falciparumBiologyIn vivoDiphtheria toxinCancer cellGlycosaminoglycanMetastasisRecombinant DNACancer researchChondroitin sulfateChondroitinCD44CancerIn vitroCell biologyMalariaImmunologyBiochemistryToxinGene

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.262
Teacher spread0.239 · 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 designBench or experimental
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

Citations219
Published2015
Admission routes2
Has abstractno

Explore more

Same venueCancer CellSame topicInvertebrate Immune Response MechanismsFrench-language works237,207