Multicenter evaluation of the Glucometer Elite XL meter, an instrument specifically designed for use with neonates.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the clinical performance of the Glucometer Elite XL Diabetes Care System in neonatal settings using a multicenter study RESEARCH DESIGN AND METHODS: A total of 388 blood specimens from 333 neonates were included in the study. A capillary or arterial sample was analyzed for determination of glucose with the Glucometer Elite XL system by an attending trained nurse. Through the same sampling site, a specimen was collected and sent to the laboratory for measurement of plasma glucose, bilirubin, and hematocrit. RESULTS: The regression analysis between the results of the Glucometer Elite XL system and comparative methods resulted in the following: Glucometer Elite XL meter = 1.01 x laboratory method + 0.02 mmol/l (n = 388). For the 1.1-4.0 mmol/l plasma glucose range, the regression was Glucometer Elite XL meter = 1.07 x laboratory method + 0.12 mmol/l (n = 150). A difference plot indicated a mean bias of 0.04 mmol/l (95% CI -0.01 to 0.10). No relationship was found between meter glucose biases and hematocrit levels (r = 0.10, P = 0.14). Although a statistically significant correlation existed between bilirubin levels and the glucose meter biases (r = 0.14, P = 0.005), the predicted mean biases were of little clinical significance. CONCLUSIONS: The Glucometer Elite XL system showed a good performance when used in neonatal settings.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".