The inclusion of capillary distribution in the adiabatic tissue homogeneity model of blood flow
Bibliographic record
Abstract
We have developed a non-invasive imaging tracer kinetic model for blood flow which takes into account the distribution of capillaries in tissue. Each individual capillary is assumed to follow the adiabatic tissue homogeneity model. The main strength of our new model is in its ability to quantify the functional distribution of capillaries by the standard deviation in the time taken by blood to pass through the tissue. We have applied our model to the human prostate and have tested two different types of distribution functions. Both distribution functions yielded very similar predictions for the various model parameters, and in particular for the standard deviation in transit time. Our motivation for developing this model is the fact that the capillary distribution in cancerous tissue is drastically different from in normal tissue. We believe that there is great potential for our model to be used as a prognostic tool in cancer treatment. For example, an accurate knowledge of the distribution in transit times might result in an accurate estimate of the degree of tumour hypoxia, which is crucial to the success of radiation therapy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".