GENERATION MECHANISMS OF SHUNT MURMURS USING FINITE ELEMENT METHOD
Bibliographic record
Abstract
At present, stethoscopy represents the only convenient and noninvasive method of assessing shunt function, but lacks quantifiability and objectivity. Therefore, as a new technique for assessing blood access function, we investigated techniques for analyzing shunt murmurs based on wavelet transformation. Results obtained using this technique were comparable to those obtained by stethoscopy and favorably represented blood flow in the shunt of dialysis patients. As few reports have described shunt murmurs and the mechanisms of onset have not been clarified, we attempted to clarify the mechanisms of onset underlying shunt murmurs using the finite element method (FEM). The present report describes the results of FEM analysis using an artificial blood vessel. High-accuracy FEM analysis requires preparation of an accurate FEM model and then simultaneous fluid analysis of blood components and structural analysis of vessel components. Since these results need to be related mutually, the fluid-solid interaction function of general-purpose FEM software was utilized. For analysis of conditions, material constants of the artificial blood vessel were determined based on measurements obtained using a microhardness tester, while inflow conditions were determined based on radial artery measurements obtained using an invasive sphygmomanometer. The results of FEM analysis broadly matched the clinical data.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".