Validation of a new generation POCT glucose device with emphasis on aspects important for glycemic control in the hospital care
Bibliographic record
Abstract
BACKGROUND: Point-of-care (POC) glucose devices are widely used for insulin-dosage decision-making although such an application is not always permitted. In this study, we have evaluated a new generation of POC glucose device, the HemoCue(®) Glucose 201DMRT (201DMRT), for its suitability for (tight) glycemic control. MATERIALS AND METHODS: This study was performed according to the CLSI/STARD criteria. The 201DMRT was compared to the laboratory hexokinase glucose method (Siemens Dimension Vista(®)). The variation among different POC devices and cuvette lot numbers was examined. Additionally, the influence of the partial pressure of oxygen and hematocrit on glucose measurement was investigated. RESULTS: The 201DMRT showed a good agreement with the laboratory reference method. This was examined using Deming regression analysis, percentage Bland-Altman plot and a modified Clarke-error grid. The total analytical error at the clinically critical glucose concentrations of 5.6, 7.0 and 11.1 mmol/L (101, 126 and 200 mg/dL) was 6.4%, 4.3% and 3.0%, respectively. The total error among the different POC devices and among different cuvette lot numbers was <6.5%. Glucose measurements on the 201DMRT were not affected by changes in partial pressure of oxygen, whereas changes in hematocrit had influence on the results (3.4% for every 0.10 L/L change in hematocrit). CONCLUSIONS: The 201DMRT device can be used for glycemic control based on analytical results presented. However, the clinical applicability for tight glycemic control must be confirmed in a clinical study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".