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Record W1982502427 · doi:10.1103/physreve.84.021919

Phenomenological model of interstitial fluid pressure in a solid tumor

2011· article· en· W1982502427 on OpenAlexaff
Long Jian Liu, Stephen L. Brown, James R. Ewing, M. Schlesinger

Bibliographic record

VenuePhysical Review E · 2011
Typearticle
Languageen
FieldMathematics
TopicMathematical Biology Tumor Growth
Canadian institutionsUniversity of Windsor
FundersNational Cancer InstituteNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsInterstitial fluidFluid dynamicsDistribution (mathematics)Measure (data warehouse)Tissue fluidFluid pressureFlow (mathematics)Biomedical engineeringPathologyMechanicsMedicineComputer sciencePhysicsMathematics

Abstract

fetched live from OpenAlex

Tumor interstitial fluid pressure (TIFP) has the potential to predict tumor response to nonsurgical cancer treatments, including radiation therapy. At present the only quantitative measures available are of limited use, since they are invasive and yield only point measurements. We present the mathematical framework for a quantitative, noninvasive measure of TIFP. The model describes the distribution of interstitial fluid pressure in three distinct tumor regions: vascularized tumor rim, central tumor region, and normal tissue. A relationship between the TIFP and the fluid flow velocity at the periphery of a tumor is presented. This model suggests that a measure of fluid flow rate from a tumor into normal tissue reflects TIFP. We demonstrate that the acquisition of serial images of a tumor after the injection of a contrast agent can provide a noninvasive and potentially quantitative measure of TIFP.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.128
GPT teacher head0.359
Teacher spread0.230 · 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 designSimulation or modeling
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

Citations25
Published2011
Admission routes1
Has abstractyes

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Same venuePhysical Review ESame topicMathematical Biology Tumor GrowthFrench-language works237,207