Reliability of point-of-care testing for glucose measurement in critically ill adults*
Bibliographic record
Abstract
BACKGROUND: Glycemic control is increasingly being recognized as a priority in the treatment of critically ill patients. Titration and monitoring of insulin infusions involve frequent blood glucose measurement to achieve target glucose ranges and prevent adverse events related to hypoglycemia. Therefore, it is imperative that bedside glucose testing methods be safe and accurate. OBJECTIVE: To determine the accuracy and clinical impact of three common methods of bedside point-of-care testing for glucose measurements in critically ill patients receiving insulin infusions. DESIGN: Prospective observational study. SETTING: A 21-bed mixed medical/surgical intensive care unit of a tertiary care teaching hospital. PATIENTS: Thirty consecutive critically ill patients who were vasopressor-dependent (n = 10), had significant peripheral edema (n = 10), or were admitted following major surgery (n = 10). MEASUREMENTS: Findings from three different methods of glucose measurement were compared with central laboratory measurements: (1) glucose meter analysis of capillary blood (fingerstick); (2) glucose meter analysis of arterial blood; and (3) blood gas/chemistry analysis of arterial blood. Patients were enrolled for a maximum of 3 days and had a maximum of nine sets of measurements determined during this time. RESULTS: Clinical agreement with the central laboratory was significantly better with arterial blood analysis (69.9% and 76.5% for glucose meter and blood gas/chemistry analysis, respectively) than with capillary blood analysis (56.8%; p = .039 and .001, respectively). During hypoglycemia, clinical agreement was only 26.3% with capillary blood analysis and 55.6% and 64.9% for glucose meter and blood gas/chemistry analysis of arterial blood (p = .010 and <.001, respectively). Glucose meter analysis of both arterial and capillary blood tended to provide higher glucose values, whereas blood gas/chemistry analysis of arterial blood tended to yield lower glucose values. CONCLUSIONS: The magnitude of the differences in the glucose values offered by the four different methods of glucose measurement led to frequent clinical disagreements regarding insulin dose titration in the context of an insulin infusion protocol for aggressive glucose control.
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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 | 0.010 | 0.059 |
| 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 | 0.001 | 0.001 |
| 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".