Imputation Model for Glucose Values Above the Upper Detection Limit for Continuous Glucose Monitors.
Bibliographic record
Abstract
Objective: All continuous glucose monitors (CGMs) have an upper detection limit, typically of 22.2 mmol/L. This might bias CGM metrics. We aimed to develop and validate a statistical model for imputing values above this limit. Methods: We analyzed CGM data from 85 inpatients with type 2 diabetes, 705 outpatients with type 1 diabetes, and 27 outpatients with type 2 diabetes. A Bayesian nonparametric latent Gaussian process regression model was applied to the CGM data intentionally right censored for the top 5%, 10%, 20%, and 30% and compared with the uncensored CGM data by the bias, mean squared error (MSE), and coefficient of determination (R2) of mean glucose, standard deviation (SD), and coefficient of variation (CV). Results: In hospitalized patients with diabetes, outpatients with type 1 diabetes, and outpatients with type 2 diabetes for 5% to 30% right censoring, respectively, the bias on mean glucose after imputation ranged from -0.012 to 0.362, -0.018 to 0.485, and -0.008 to 0.130, respectively. Bias on SD ranged from -0.024 to 0.226, -0.033 to 0.381, and -0.016 to 0.138, respectively. Bias on CV ranged from -0.207 to 1.543, -0.316 to 2.609, and -0.222 to 1.721, respectively. Similar results indicating good performance of the imputation model were observed for MSE and R2. Conclusions: An imputation model for glucose values above the upper detection limit of CGMs was developed and validated in various populations. This enables a more unbiased quantification of CGM metrics for patients with severe hyperglycemia.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".