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Record W2092072425 · doi:10.4103/1477-3163.145609

The advent of precision therapy in gastrointestinal malignancies: Targeting the human epidermal growth factor receptor family in colorectal and esophagogastric cancer

2014· review· en· W2092072425 on OpenAlex
Piotr Czaykowski, Danielle Desautels, Craig Harlos

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

VenueJournal of Carcinogenesis · 2014
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsMedicineColorectal cancerBevacizumabTargeted therapyEpidermal growth factor receptorCancerGastrointestinal cancerOncologyMonoclonal antibodyEpidermal growth factorPrecision medicineInternal medicineChemotherapyCancer researchAntibodyReceptorImmunologyPathology

Abstract

fetched live from OpenAlex

Until recently, systemic therapy for gastrointestinal malignancies was restricted to relatively noncancer-specific cytotoxic chemotherapy. Over the last 15 years targeted therapies have become available, most notably bevacizumab in the case of advanced colorectal cancer. Unfortunately, there are no predictive biomarkers to guide the use of this agent. In this review article, we describe the advent of "Precision Medicine" (in part, the use of patient-specific molecular markers to inform treatment) in gastrointestinal cancers: The use of monoclonal antibodies targeting epidermal growth factor receptor in advanced colorectal cancer, and human epidermal growth factor receptor 2-neu in advanced esophagogastric cancer. In both instances, biomarkers help in selecting appropriate patients for such treatment.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.335
Teacher spread0.292 · 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