Trial reportDiabetes technology & therapeutics2017
Stability, Accuracy, and Risk Assessment of a Novel Subcutaneous Glucose Sensor.
Trial report in Diabetes technology & therapeutics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cutaneous Complications With Continuous or Flash Glucose Monitoring Use: Systematic Review of Trials and Observational Studies.Journal of diabetes science and technology · 2020Pooled it
- Time in range-A new gold standard in type 2 diabetes research?Diabetes, obesity & metabolism · 2025Review
- Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting.Journal of diabetes science and technology · 2023Article
- Toward a Framework for Outcome-Based Analytical Performance Specifications: A Methodology Review of Indirect Methods for Evaluating the Impact of Measurement Uncertainty on Clinical Outcomes.Clinical chemistry · 2019Review
- The Future of Continuous Glucose Monitoring.Diabetes technology & therapeutics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUsers of continuous glucose monitoring (CGM) systems are concerned with the frequency of inserting and calibrating new sensors, with sensor accuracy and reliability throughout the sensor's functional life, and with the risks associated with inaccurate sensor readings.
methodsA sensor for our next-generation CGM system was tested for accuracy by comparison with self-monitored blood glucose (SMBG) values throughout 10 days of wear. Fifty subjects (49 with type 1 diabetes, 1 with type 2 diabetes, 20 male, mean ± standard deviation [SD] age 32.5 ± 18.7 years) enrolled. Subjects wore one sensor each, calibrated it once per day, and obtained multiple daily SMBG values for comparison. A total of 2739 paired CGM-SMBG values were analyzed to arrive at standard accuracy statistics and plotted on the surveillance error grid (SEG) to estimate the risk of SMBG-CGM discrepancies.
resultsThe overall mean and median absolute relative difference (ARD) values were 9.6% and 7.2%, respectively. The median ARD values ranged from 8.9% on Day 1 to 6.5% on Day 10. SEG analysis categorized 2727 points (99.6%) as "no" or "slight" risk and 12 points (0.4%) as "moderate" or "great" risk. Thirty-nine (79.6%) of the 49 systems worked through the end of Day 10. Sensors and adhesives were well tolerated, with minimal erythema and induration.
conclusionsThis new CGM system's accuracy throughout its 10-day functional life, the convenience associated with once-daily calibrations, and the high proportion of measurements in the "no risk" zone of the SEG support its nonadjunctive use in diabetes management and closed-loop insulin delivery systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.