Accuracy of Glycemic Measurements in the Critically Ill
Bibliographic record
Abstract
BACKGROUND: Recent evidence emphasizes the importance of maintaining normoglycemia in critically ill patients to reduce morbidity and mortality. Different analytical methods of varying accuracy exist for obtaining and measuring blood glucose in critically ill patients. The purpose of this study was to determine if there were differences in blood glucose values measured by whole blood capillary and arterial samples using three different bedside blood glucose meters and a blood gas analyzer as compared to a reference blood glucose analyzer. METHODS: Sixty subjects were recruited from a university hospital medical/surgical intensive care unit. Matching capillary and arterial samples were analyzed by a clinical blood glucose reference analyzer (YSI, Yellow Springs Instrument, Yellow Springs, OH) and three blood glucose meters (Lifescan [Milpitas, CA] SureStepFlexx, Roche [Indianapolis, IN] Accu-Chek Inform, and Abbott [Alameda, CA] FreeStyle). Additionally, the arterial samples were analyzed by a point-of-care blood gas analyzer (Chiron 865, Bayer, Tarrytown, NY). RESULTS: Data analysis included repeated-measures analysis of variance, Consensus Error Grid analysis, Bland-Altman plots, and numerical estimates of inaccuracy. With capillary samples there were high numbers of errors as compared to the reference instrument. Measurement of blood glucose with arterial samples demonstrates a higher degree of accuracy. With arterial samples, the Abbott FreeStyle blood glucose meter and the blood gas analyzer glucose exhibited the lowest median and mean relative absolute deviation. CONCLUSION: In critically ill adult patients, measurement of blood glucose using arterial samples is recommended. Using arterial blood, the Abbott FreeStyle blood glucose meter and the point-of-care blood gas analyzer (Bayer Chiron 865) were shown to be highly accurate instruments to measure arterial blood glucose.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".