Development of a new method for monitoring blood purification: The blood flow analysis of the head and foot by laser Doppler blood flowmeter during hemodialysis
Bibliographic record
Abstract
The management of blood pressure in hemodialysis patients greatly affects not only their quality of life, but also the duration of dialysis (dialysis life). Approximately 25% of dialysis patients suffer from continuous low blood pressure; however, the relationship between blood pressure and blood flow during dialysis has not been established. We hypothesized that with complete extracorporeal circulation, blood purification methods might affect blood flow, resulting in a change in blood pressure. The purpose of this study was to develop a noninvasive continuous monitoring method (NICOMM) as a microcirculation monitor by devising a laser-Doppler flowmeter (LDF) system with a wavelength of 780 nm. The aim was to use this system to simultaneously measure blood flow rate in both the head and the foot during dialysis and to determine the effectiveness of NICOMM by measuring blood flow and arterial distensibility. When exhibiting a significant decline in blood pressure at 240 min after the initiation of hemodialysis, a drop in blood flow in parallel with the blood pressure fall was recorded by the LDF. However, no changes were observed, in the readings by a continuous hematocrit monitor (Crit-Line monitor, CLM). Furthermore, a significant correlation was registered between mean arterial blood pressure and blood flow rate in the earlobe tissue from 180 min after the initiation of hemodialysis to the completion of hemodialysis (p < 0.001, r = 0.78). Comparisons were made by measuring vascular dehydration using the CLM. NICOMM showed more stable readings than the CLM in monitoring blood flow in response to changes in blood pressure.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".