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Record W1994040984 · doi:10.1177/1078155207080804

Overview of targeted therapies in Oncology

2007· article· en· W1994040984 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

VenueJournal of Oncology Pharmacy Practice · 2007
Typearticle
Languageen
FieldMedicine
TopicCancer Research and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineMonoclonal antibodyAngiogenesisTargeted therapyCancerPharmacologyCancer therapyOncologyBioinformaticsCancer researchImmunologyInternal medicineAntibody

Abstract

fetched live from OpenAlex

BACKGROUND: Recent scientific advances have provided a map of the human genome along with a better understanding of the processes that transform healthy cells into diseased cells. This has led to the emergence of a new class of drugs called targeted therapies. OBJECTIVE: To describe the classifications and basic pharmacology of targeted therapies. METHODS: A literature search was performed for peer reviewed journal articles using Medline (1996-2007), Embase (1996-2007) and Google. The search was performed using keywords such as angiogenesis inhibitors, cancer vaccines, gene therapy, monoclonal antibodies, small molecules, proteasome inhibitors, targeted therapy and tyrosine kinase inhibitors. CONCLUSIONS: A review of the basic pharmacology is described in this article, including the following major categories of targeted therapies: * Small molecule drugs * Monoclonal antibodies * Apoptosis-inducing drugs * Angiogenesis Inhibitors * Cancer vaccines * Gene therapy.

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)0.004
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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)0.0010.000

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 head0.163
GPT teacher head0.554
Teacher spread0.391 · 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