Radionuclide plethysmography for noninvasive evaluation of peripheral arterial blood flow
Bibliographic record
Abstract
We validated a noninvasive radionuclide plethysmography technique to evaluate peripheral arterial blood flow during reactive hyperemia. This method, based on the measurement of blood volume variations during repetitive venous occlusions, was compared with strain-gauge venous impedance plethysmography. The technique uses 99mTc-labeled autologous red blood cells scintigraphy to determine the rate of change of forearm scintigraphic counts during venous occlusion. Thirteen subjects were simultaneously evaluated with radionuclide and impedance plethysmography. Six baseline flow measurements were performed to evaluate the reproducibility of each method. Twenty-seven serial measurements were then made to evaluate flow variation during forearm reactive hyperemia. After 30 min of recovery, resting forearm blood flows were again evaluated. Impedance and radionuclide methods showed excellent reproducibility with intraclass correlation coefficients of 0.96 and 0.93, respectively. There was also good correlation of flows between both methods during reactive hyperemia (r = 0.87). Resting flows at 30 min after reactive hyperemia were slightly lower than at baseline with both methods. We conclude that radionuclide plethysmography could be used for the noninvasive evaluation of forearm blood flow and its dynamic variations during reactive hyperemia.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".