A User-Friendly Web Tool for Custom Analysis of Continuous Glucose Monitoring Data
Bibliographic record
Abstract
Abstract Background Continuous Glucose Monitoring (CGM) systems have revolutionized diabetes management by providing real-time blood glucose tracking. However, there is a need for openly accessible tools that can analyze CGM data in relation to specific events like meals or exercise, which often require extensive technical skills to interpret, thus restricting its broader use among researchers and clinicians. Developing user-friendly web applications to facilitate this analysis could significantly broaden accessibility and utility. Method Diametrics was built with a focus on ease-of-use and versatility. The application’s efficacy was validated against iglu , an established R tool with a no-code web app for CGM analysis, using data from 418 participants from three studies. The unique period-specific analysis feature was demonstrated through an illustrative case study. Results Diametrics proved effective at replicated established CGM metrics, demonstrating high concordance with iglu . The platform supports a wide range of CGM devices, accommodates data in various formats, and offers extensive customization in the analysis settings. The case study highlighted Diametrics’ ability to integrate exercise-related data with CGM readings, enabling detailed analyses of how different exercise types, intensities, and times of day impact glucose levels. Conclusions Diametrics is a freely available, reproducible, user-friendly, and accurate web-based tool for CGM data analysis with a unique capability to analyze data over specific time periods. With its intuitive design and open-source accessibility, Diametrics provides a valuable resource in diabetes research and management, empowering users of various technical levels to perform complex analyses with ease.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.098 | 0.057 |
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".