Assessment of resistance vessel function in human skeletal muscle: guidelines for experimental design, Doppler ultrasound, and pharmacology
Bibliographic record
Abstract
The introduction of duplex Doppler ultrasound almost half a century ago signified a revolutionary advance in the ability to assess limb blood flow in humans. It is now widely used to assess blood flow under a variety of experimental conditions to study skeletal muscle resistance vessel function. Despite its pervasive adoption, there is substantial variability between studies in relation to experimental protocols, procedures for data analysis, and interpretation of findings. This guideline results from a collegial discussion among physiologists and pharmacologists, with the goal of providing general as well as specific recommendations regarding the conduct of human studies involving Doppler ultrasound-based measures of resistance vessel function in skeletal muscle. Indeed, the focus is on methods used to assess resistance vessel function and not upstream conduit artery function (i.e., macrovasculature), which has been expertly reviewed elsewhere. In particular, we address topics related to experimental design, data collection, and signal processing as well as review common procedures used to assess resistance vessel function, including postocclusive reactive hyperemia, passive limb movement, acute single limb exercise, and pharmacological interventions.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".