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Record W1967587727 · doi:10.1126/science.1125950

Antiangiogenic Therapy: A Universal Chemosensitization Strategy for Cancer?

2006· article· en· W1967587727 on OpenAlexaff
Robert S. Kerbel

Bibliographic record

VenueScience · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineChemotherapyCancerClinical trialCancer therapyCancer cellCytotoxic T cellPharmacologyRepopulationCancer researchOncologyInternal medicineBiologyIn vitroStem cell

Abstract

fetched live from OpenAlex

For more than 50 years, a major goal of research in cancer therapeutics has been to develop universally effective agents that render cancer cells more sensitive to cytotoxic chemotherapy without substantially increasing toxicity to normal cells. The results of recent clinical trials indicate that certain antiangiogenic drugs may produce this long-sought effect. Here, I describe three distinct mechanisms that may help to explain the chemosensitizing activity of these drugs: normalizing tumor vasculature, preventing rapid tumor cell repopulation, and augmenting the antivascular effects of chemotherapy. I then discuss how these potential mechanisms might be exploited to maximize therapeutic efficacy.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.002
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 head0.017
GPT teacher head0.283
Teacher spread0.266 · 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 designBench or experimental
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

Citations470
Published2006
Admission routes1
Has abstractyes

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