Continuous glucose monitoring versus fasting blood glucose basal insulin titration: a retrospective analysis
Bibliographic record
Abstract
AIMS: Continuous glucose monitoring (CGM) may complement or potentially replace fasting blood glucose (FBG) for basal insulin dose titration in type 2 diabetes (T2D). This retrospective analysis compared CGM-based titration with FBG-based titration using 7354 pairs of FBG and blinded CGM data from a clinical study in 68 people with T2D. METHODS: Based on pharmacokinetic/pharmacodynamic simulations, the median of 3 lowest CGM values in the hour preceding the FBG timepoint ("1-h am nadir") was selected as basis for titration. Basal insulin doses were determined using 3 algorithms (Canadian INSIGHT, Treat2Target, AT.LANTUS). Absolute/relative dose adjustment differences, mean absolute relative differences, and relative dose errors between CGM- and FBG-based doses were calculated. RESULTS: The 1-h am nadir was essentially equivalent to FBG for basal titration across all 3 algorithms. There was >90 % probability of the absolute dose adjustment difference being within tolerance. Mean absolute relative difference values were generally low, although higher for Treat2Target. Relative dose errors were mostly between -10 % and 10 %, indicating high agreement between CGM- and FBG-based titration and low clinical risk. CONCLUSIONS: This study established that the 1-h am nadir can potentially be used as an FBG surrogate for basal insulin titration in T2D.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".