Motorized adaptive compression system for enhancing venous return: A feasibility study on healthy individuals
Bibliographic record
Abstract
Notwithstanding the extensive use of conventional compression devices in managing venous disorders, these modalities have shortages that diminish their treatment efficacy and lessen patient adherence to therapy. The purpose of this study was to develop an improved compression system that eliminates the flaws of the existing devices. A motorized bandage was designed that takes advantage of continuous feedback from force-sensing resistors to apply reproducible, controlled pressure on the lower extremities. The performance of the device in enhancing venous return was explored in a pilot test on 11 healthy participants, wherein graded lower body negative pressure was employed as a surrogate of passive standing. Each subject underwent two experiments; with and without pressure application over the calves. A two-way repeated-measures analysis of variance revealed a significant difference in the mean hemodynamic responses when the compression bandage was in action (p < .05). Specifically, a meaningful increase was observed in mean arterial pressure by 5%, diastolic blood pressure by 8% and left ventricular ejection time by 4%; and a significant decrease of 5% and 6% was noticed in heart rate and pulse pressure, respectively. These results demonstrate the capability of the designed system in attenuating the imposed orthostatic stress on cardiovascular system.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".