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Record W2084255745 · doi:10.3747/co.v17i4.696

Cetuximab in Locally Advanced Head-And-Neck Cancer: Defining the Population

2010· article· en· W2084255745 on OpenAlexaffvenueabout
Clement Ho

Bibliographic record

VenueCurrent Oncology · 2010
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsCetuximabMedicineHead and neck cancerOncologyEpidermal growth factor receptorHead and neckHead and neck squamous-cell carcinomaSquamous cell cancerCancerPopulationInternal medicineRadiation therapyTargeted therapyGuidelineEGFR inhibitorsClinical trialColorectal cancerPathologySurgery

Abstract

fetched live from OpenAlex

Encouraging data for targeted therapy in head-and-neck squamous cell carcinoma are opening new options for treatment. Phase III trials of cetuximab, an antibody directed against the epidermal growth factor receptor (EGFR) have demonstrated benefit in the locally advanced and metastatic settings. Recognizing the importance of emerging therapies, Cancer Care Ontario published guideline recommendations for EGFR-targeted therapy in stage III and IV head-and-neck cancer. The present paper takes a further look at the population for whom an offer of cetuximab therapy may be appropriate.

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)0.008
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.060
GPT teacher head0.425
Teacher spread0.365 · 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

Citations8
Published2010
Admission routes3
Has abstractyes

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