Economic analyses of freestyle libre systems for people living with diabetes: a systematic literature review
Bibliographic record
Abstract
INTRODUCTION: This study systematically reviewed economic evaluations of FreeStyle Libre systems (FSL) for glucose monitoring among people living with type 1 (T1DM) or type 2 diabetes mellitus (T2DM). METHODS: Systematic searches were conducted on 27 February 2024 using the MEDLINE, Embase, and Cochrane databases. Study selection was conducted by two reviewers who independently reviewed titles, abstracts, and full article texts. Study quality was assessed using the Consolidated Health Economic Evaluation Reporting Standards 2022 checklist. RESULTS: In total, 18 cost-effectiveness studies were included; 17 were based on data for the original FSL device, with only one using FSL2 data. There were no major issues with study quality. The ten studies comparing FSL with self-monitoring of blood glucose (SMBG) reported FSL to be cost effective in populations with T1DM (seven studies) or T2DM (six studies). Eight studies reported other sensor-based systems to be cost effective versus FSL. CONCLUSIONS: Existing evidence suggests FSL systems are cost effective versus SMBG among people living with T1DM or T2DM on intensive insulin. Additional studies are needed to compare FSL systems with other sensor-based systems, considering the range of available evidence, the appropriateness of the compared devices, and the selection of modelling assumptions.
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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 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".