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Record W1968430623 · doi:10.3747/co.v17is1.616

Efficacy of the Monoclonal Antibody EGFR Inhibitors for the Treatment of Metastatic Colorectal Cancer

2010· article· en· W1968430623 on OpenAlexaffvenueabout
Marwan Fakih

Bibliographic record

VenueCurrent Oncology · 2010
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsCancerCare Manitoba
Fundersnot available
KeywordsPanitumumabCetuximabMedicineIrinotecanKRASColorectal cancerMonoclonal antibodyOncologyEpidermal growth factor receptorInternal medicineEGFR inhibitorsCancer researchCancerAntibodyImmunology

Abstract

fetched live from OpenAlex

Two anti-epidermal growth factor receptor (EGFR) monoclonal antibodies (MoAbs) have been approved in Canada for the treatment of metastatic colorectal cancer (mCRC) - cetuximab, a mouse-human chimeric MoAb, and panitumumab, a fully human MoAb. This paper reviews the efficacy of the anti-EGFR monoclonal antibodies cetuximab and panitumumab - both as monotherapy and in combination with cytotoxic chemotherapy - in the treatment of mCRC. Both cetuximab and panitumumab have demonstrated clinical efficacy in monotherapy in patients with mCRC, an advantage that has recently been found to be limited largely to those with wild-type KRAS tumors. Advantages of using these agents in monotherapy include reduced cost and toxicity. While the addition of cetuximab to irinotecan has shown superior progression-free survival and response compared with cetuximab monotherapy, there is currently no evidence for a benefit of panitumumab in combination with irinotecan.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.092
GPT teacher head0.448
Teacher spread0.356 · 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

Citations75
Published2010
Admission routes3
Has abstractyes

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