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Record W1997923530 · doi:10.1111/odi.12246

Defining the genomic landscape of head and neck cancers through next‐generation sequencing

2014· review· en· W1997923530 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOral Diseases · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsLawson Health Research InstituteCancer Care OntarioWestern University
Fundersnot available
KeywordsHead and neck squamous-cell carcinomaHead and neck cancerDNA sequencingPersonalized medicineGenomicsBiologyCancerSingle cell sequencingGenomic sequencingComputational biologyPrecision medicineBioinformaticsCancer researchMedicineMutationExome sequencingGenomeGeneticsGene

Abstract

fetched live from OpenAlex

Next-generation sequencing (NGS) has revolutionized the field of genomics and improved our understanding of cancer biology. Advances have been achieved by sequencing tumor DNA and using matched normal DNA to filter out germ line variants to identify cancer-specific changes. The identification of high incidences of activating mutations in head and neck squamous cell carcinoma (HNSCC) amenable to drug targeting has been made, with clear distinctions between the mutational profile of HPV-positive and HPV-negative tumors. This wealth of new understanding undoubtedly ameliorates our understanding of HNSCC cancer biology and elucidates clear targets for drug targeting which will guide future personalized medicine.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.043
GPT teacher head0.312
Teacher spread0.269 · 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