Modeling Mechanical Determinants of Skeletal Muscle Perfusion
Bibliographic record
Abstract
Current understanding of vasomotor regulation in skeletal muscle is based largely on changes in vascular resistance (R) that, in turn, reflects changes in vessel caliber. While this deals adequately with the steady component of flow, it neglects vessel compliance (C) which is a major determinant of the oscillatory component of flow. In this study we consider a modified Windkessel model (RCKL) in which the effects of R and C are examined along with those of fluid inertia (L) and viscoelasticity (K) of the vessel wall. The model is used to calculate a flow wave that results from a measured pressure wave; the computed wave is verified by comparison with one measured concurrently with the pressure wave. Values of R,C,K,L, required to produce agreement between the two waveforms are then recorded. The model was tested using brachial artery pressure (Millar) and flow (Doppler ultrasound) waveforms that were measured concurrently in a single individual during before and after altering R using wrist occlusion, and with the arm positioned level with, and above, heart level to change C independently of R. In (1) R increased 4‐fold with minimal change in C, while in (2) C increased 2‐fold with no change in R. Thus, the model offers an extended probe to study vasomotor regulation. Supported by the NSERC‐CIHR Collaborative Health Research Program
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".