Real‐world glycaemic outcomes in adult persons with type 1 diabetes using a real‐time continuous glucose monitor compared to an intermittently scanned glucose monitor: A retrospective observational study from the Canadian LMC diabetes registry (REAL‐CGM‐T1D)
Bibliographic record
Abstract
Abstract Real‐time continuous glucose monitoring (rtCGM) and intermittently scanned CGM (isCGM) have both been shown to improve glycaemic outcomes in people with T1D. The aim of this study was to compare real‐world glycaemic outcomes at 6–12 months in a propensity score matched cohort of CGM naïve adults with T1D who initiated a rtCGM or an isCGM. Among the matched rtCGM and isCGM cohorts (n = 143/cohort), rtCGM users had a significantly greater HbA1c benefit compared to isCGM users (adjusted difference, −3 mmol/mol [95% CI, −5 to −1]; −0.3% [95% CI, −0.5 to −0.1]; p = 0.01). There was a significantly greater lowering of HbA1c for rtCGM compared to isCGM when baseline HbA1c was <69 mmol/mol (8.5%) (adjusted difference, −4 mmol/mol [95% CI, −7 mmol/mol to −2 mmol/mol]; −0.4% [95% CI, −0.6% to −0.2%]; p < 0.001), and in MDI users (adjusted difference, −3 mmol/mol [95% CI, −6 mmol/mol to −0 mmol/mol]; −0.3% [95% CI ‐0.5% to 0.0%], p = 0.04). The rtCGM cohort had significantly greater time in range (58.3 ± 16.1% vs. 54.5 ± 17.1%, p = 0.03), lower time below range (2.1 ± 2.7% vs. 6.1 ± 5.0%, p < 0.001) and lower glycaemic variability compared to the isCGM cohort. In this real‐world analysis of adults with T1D, rtCGM users had a significantly greater reduction in HbA1c at 6–12 months compared to isCGM, and significantly greater time in range, lower time below range and lower glycaemic variability, compared to a matched cohort of isCGM users.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".