Model Simulation of Forearm Hyperaemic Reactivity
Bibliographic record
Abstract
Forearm hyperaemic reactivity (FHR) has been proposed as a novel noninvasive method for discriminating patients with cardiovascular disease (CVD). However, the modeling functions of FHR require more robust models. The present study was designed to develop quantitative modeling techniques to better estimate the physiology of this model. The fitted time activity curves of the hyperaemic arm of non- CVD participants, using blood and muscle uptake, were obtained in the 2-compartment model with the mean R 2 =0.913±0.018. However, for CVD patients, the 2- compartment model yielded a mean R 2 =0.844±0.018, so a 3-compartment model was used. This model generated mean R 2 of 0.982±0.002 for non-CVD participants and 0.979±0.002 for CVD patients. It is believed that 3- compartment model provides estimates of the activity in the blood, in the interstitial space or cytoplasm, and in the mitochondria. The 2-compartment model provides good fits for FHR in non-CVD participants but not CVD patients. Alternatively, it would seem that the 3-compartment model provides good fits for both groups. These results should help us optimize the predictive values of the FHR test, infer pathological components of the disease and, ultimately improve the patient risk stratification.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".