Biophysical Model of Interstitial Fluid Pressure in Cervical Tumors
Bibliographic record
Abstract
Interstitial fluid pressure (IFP) was measured prior to treatment in patients with cervix cancer at Toronto’s Princess Margaret Hospital as part of a prospective clinical study. The results showed that there was a strong correlation between elevated IFP and patient survival, regardless of other prognostic factors such as patient age, stage or lymphatic involvement. With a view to understanding this important correlation, we developed a biophysical model of interstitial fluid flow in cancer tissue based on Darcy’s law (a mathematical law developed originally to describe water flow through porous media). Flow through the pressure-recording device was also modeled. The result was a mathematical expression showing how measured IFP changes as a function of time, after insertion of the measurement needle into the tumor. By analyzing 152 pressure-time curves observed in this manner, we were able to show (i) that the time constant governing the rise of measured pressure has no correlation with the steady state IFP; and (ii) that the steady IFP does not depend in any significant way on the cellular morphology or the hydraulic conductivity of the interstitium, but is more dependent on the regional vascular pressure. Our findings suggest that the source of the observed elevation of IFP in cancer patients relates to the nature and distribution of tumor vasculature, rather than to properties of cancer cells themselves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".