An insulin-dose error assessment grid: A new tool to evaluate glucose meter performance
Bibliographic record
Abstract
OBJECTIVE: To develop a tool to assess the clinical accuracy of glucose meter performance using an insulin dosing protocol to assess the frequency and extent of error in insulin dose categories. METHODS: Retrospective comparison of 1815 glucose meter and central laboratory glucose results obtained from 1698 critically ill patients was conducted using the Parkes error grid, Surveillance error grid and an insulin dose error assessment grid with a sliding scale insulin dosing protocol used to manage critically ill patients. RESULTS: Parkes error grid and Surveillance error grid analyses indicated little risk conferred with the glucose meter results. Insulin dose error assessment grid complemented the aforementioned consensus error grids by determining quantifiable metrics, insulin dose category errors. Insulin dose error analysis indicated that 76.8% (1395/1815) would not have any change in insulin dose, 99.2% (1800/1815) within ±1 insulin dose category, 99.9% (1814/1815) within ±2 categories and 100% within ±3 insulin dose categories. CONCLUSIONS: Analysis with an insulin dose error grid provides information about the frequency and extent of insulin dose category errors with a specific insulin dosing protocol and describes potential clinical impact of glucose meter error.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".