Construction of a vascularized cardiopulmonary resuscitation mannequin with hemodynamic monitoring including peripheral vasculature
Bibliographic record
Abstract
Abstract Introduction: Feedback devices for cardiopulmonary resuscitation (CPR) currently register compression rate, depth, recoil and land marking. There remains a gap in determining the impact of peripheral vascularization, blood pressure, and blood flow as a result of quality CPR compressions.Methods: Our team designed a closed-loop CPR mannequin model that represented the vascularization of a human, including peripheral lower limbs. A disposable, ultrasound bandage (Flosonics Flopatch™) was applied to measure the blood flow. The model consisted of a CPR mannequin and feedback software, pressure monitoring device, patient monitor, Polyvinyl chloride (PVC) tubing and connectors, siphon bulb, 3D printed parts and wood for stabilization, Kelly clamps, and water mixture to replicate blood. A full cost breakdown and set-up is provided.Results: 28 Basic Life Saving-trained individuals tested the device both clamped and unclamped to peripheral vasculature. CPR was performed for 5 minutes at 60bpm to mimic human heart rate with siphon bulb limitations. Findings demonstrate that pulse pressure mean was 69.9mmHg clamped and 65.0mmHg unclamped (p = 0.03), consistent with expected values anticipated during effective compressions. Blood flow velocity was statistically insignificant, and cannot be inferred on due to inconsistencies with the ultrasound bandage.Conclusions:The CPR vascularization prototype was effective in replicating blood pressures of a human adult circulatory system, including peripheral vasculature. There remains limitations to state the model was effective for replicating blood flow velocity with the Flopatch™, further testing is required. The use of Kelly clamps was effective in restricting blood flow to tube sections.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".