Endogenous factors modified by hemodialysis may interfere with the accuracy of blood glucose‐measuring device
Bibliographic record
Abstract
In Japan, self-monitoring of blood glucose (SMBG) devices are widely used both at home and in hospitals, but many analytical errors and safety concerns have been reported about the SMBG devices used in hospitals. Analytical performances of StatStrip (Nova Biomedical Corporation, MA, USA), a new point-of-care testing device and Glutest (Sanwa Chemical, Aichi, Japan), a routinely used SMBG device were compared in glucose measurement of pre- and postdialysis blood samples and we evaluated which factors in blood modified by hemodialysis affect accuracy of these devices. Subjects in this study were 44 hemodialysis patients. Blood samples were obtained from patients just before and just after the hemodialysis. Blood glucose concentrations of samples were measured by StatStrip and Glutest. Hematocrit and plasma concentrations of electrolytes, metabolites, etc. of the samples were measured in the central laboratory. StatStrip showed no difference between pre- and postdialysis blood samples and showed very little bias from reference method. On the other hand, Glutest showed difference between pre- and postdialysis samples. Although there is no problem in the data of predialysis blood samples by Glutest, however, these of the postdialysis blood samples by Glutest were >10% less than reference method. Factors in blood modified by hemodialysis such as hematocrit, uric acid, albumin, potassium, and calcium affected glucose readings by Glutest. Glucose readings by Glutest of samples from hemodialysis patients were affected by hematocrit and several factors, which were modified by hemodialysis. StatStrip is considered as a better device in dialysis hospitals.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".