Arterial flow measurements during reactive hyperemia using NIRS
Bibliographic record
Abstract
Non-invasive evaluation of peripheral perfusion may be useful in many contexts including clinical research. We validated a novel non-invasive spectroscopy technique to quantify forearm arterial inflow. This method, which is based on the measurement of tissular total hemoglobin variations after an ischemic period, was compared to strain gauge plethysmography (SGP). The technique uses near-infrared spectroscopy (NIRS) to determine the rate of change of forearm tissue oxygenation during reactive hyperemia. In this study, 13 subjects were simultaneously evaluated with NIRS and SGP. Nine baseline flow measurements were performed to assess the reproducibility of each method. Twenty-seven serial measurements were then made to evaluate flow variation during forearm reactive hyperemia. SGP and NIRS methods showed excellent reproducibility with the same intra-class correlation coefficients (0.98). In conclusion, the NIRS technique appears well suited for non-invasive evaluation of quantitative arterial forearm flow.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".