MétaCan
Menu
Back to cohort
Record W2779612734 · doi:10.1097/fpc.0000000000000318

Genome-wide meta-analyses identifies novel taxane-induced peripheral neuropathy-associated loci

2017· review· en· W2779612734 on OpenAlexaff
Lara E. Sucheston‐Campbell, Alyssa Clay‐Gilmour, William E. Barlow, G. Thomas Budd, Daniel O. Stram, Christopher A. Haiman, Xin Sheng, Li Yan, Gary Zirpoli, Song Yao, Chen Jiang, Kouros Owzar, Dawn L. Hershman, Kathy S. Albain, Daniel F. Hayes, Halle C. F. Moore, Timothy J. Hobday, James A. Stewart, Abbas Rizvi, Claudine Isaacs, Muhammad Salim, Jule R. Gralow, Gabriel N. Hortobágyi, Robert B. Livingston, Deanna L. Kroetz, Christine B. Ambrosone

Bibliographic record

VenuePharmacogenetics and Genomics · 2017
Typereview
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsSaskatchewan Cancer Agency
FundersNational Cancer InstituteNational Institutes of Health
KeywordsTaxaneMedicineChemotherapy-induced peripheral neuropathyPeripheral neuropathyInternal medicineOncologyOdds ratioCommon Terminology Criteria for Adverse EventsBreast cancerGenome-wide association studyConfidence intervalCancerPharmacogeneticsDiabetes mellitusGeneticsSingle-nucleotide polymorphismGenotypeBiologyGeneEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVE: Taxane containing chemotherapy extends survival for breast cancer patients. However, taxane-induced peripheral neuropathy (TIPN) cannot be predicted, prevented or effectively treated. Using genome-wide analyses, we sought to identify common risk variants for TIPN. PATIENTS AND METHODS: Women with high-risk breast cancer enrolled in SWOG 0221 were genotyped using the Illumina 1M chip. Genome-wide analyses were performed in relation to ≥grade 3 Common Terminology Criteria for Adverse Events (CTCAE) neuropathy in European and African Americans. Data were meta-analyzed with GW associations of CTCAE ≥grade 3 versus

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)0.005
metaresearch head score (Gemma)0.009
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: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.015
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.450
GPT teacher head0.487
Teacher spread0.037 · 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
GenreReview

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

Citations35
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuePharmacogenetics and GenomicsSame topicCancer Treatment and PharmacologyFrench-language works237,207