Continuous glucose monitoring in noninsulin‐treated type 2 diabetes: A critical review of reported trials with an updated systematic review and meta‐analysis of randomised controlled trials
Bibliographic record
Abstract
Abstract Aims We aimed to review the observational and randomised clinical trial evidence and provide pragmatic recommendations for using continuous glucose monitoring (CGM) in individuals living with noninsulin‐treated type 2 diabetes (T2DM). Materials and Methods We first undertook a narrative review of observational studies that enrolled noninsulin‐users or mixed populations of noninsulin and insulin‐users with T2DM as well as randomised controlled trials (RCTs) that enrolled mixed populations with T2DM. We then performed a systematic review of the RCTs that specifically enrolled noninsulin‐treated populations with T2DM and compared CGM to BGM/usual care. A meta‐analysis of glycaemic outcomes was conducted with predefined subgroups based on CGM type. Results RCTs in mixed populations and observational studies demonstrated a largely consistent benefit of CGM on glycaemic and nonglycaemic outcomes with cost effectiveness and reduced healthcare resource utilisation. The meta‐analysis of RCTs in noninsulin users included 8 studies encompassing 541 participants, among whom 297 (55%) were assigned to the CGM group. CGM was associated with significantly reduced HbA1c (weighted mean difference [WMD] −0.37%; 95% CI −0.49, −0.24; p < 0.00001; I 2 = 0%), increased % time in range (WMD 8.84; 95% CI 4.62, 13.06; p < 0.0001; I 2 = 0%) and lower % time above range (WMD −8.14; 95% CI −12.66, −3.63; p = 0.0004; I 2 = 0%). There were no significant subgroup differences. Conclusions CGM use in noninsulin‐treated individuals living with T2DM was associated with improved glycaemic outcomes and patient experience, reduced health care resource utilisation, and acceptable cost‐effectiveness. These findings provide additional evidence to support CGM use among people living with T2DM who are not using insulin therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.022 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".