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Record W1989792740 · doi:10.3389/fonc.2013.00154

The Tumor Microenvironment and Strategies to Improve Drug Distribution

2013· article· en· W1989792740 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoOntario Institute for Cancer Research
FundersCanadian Institutes of Health Research
KeywordsDrugTumor microenvironmentDistribution (mathematics)MedicineTumor heterogeneityComputer scienceCancer researchPharmacologyCancerInternal medicineTumor cells

Abstract

fetched live from OpenAlex

The microenvironment within tumors is composed of a heterogeneous mixture of cells with varying levels of nutrients and oxygen. Differences in oxygen content result in survival or compensatory mechanisms within tumors that may favor a more malignant or lethal phenotype. Cells that are rapidly proliferating are richly nourished and preferentially located close to blood vessels. Chemotherapy can target and kill cells that are adjacent to the vasculature, while cells that reside farther away are often not exposed to adequate amounts of drug and may survive and repopulate following treatment. The characteristics of the tumor microenvironment can be manipulated in order to design more effective therapies. In this review, we describe important features of the tumor microenvironment and discuss strategies whereby drug distribution and activity may be improved.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.008
GPT teacher head0.290
Teacher spread0.282 · 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