Evaluation of Conventional Blood Glucose Monitoring as an Indicator of Integrated Glucose Values Using a Continuous Subcutaneous Sensor
Bibliographic record
Abstract
OBJECTIVE: To use a portable continuous glucose monitoring system (CGMS) to evaluate how well the customary intermittent self-monitoring of blood glucose (SMBG) correlates with integrated values during the surrounding time periods in ambulatory patients with type 1 diabetes. RESEARCH DESIGN AND METHODS: In the study, 18 young patients with type 1 diabetes were monitored with CGMS for up to 72 h, during which they continued to perform the four standard SMBG tests (preprandial and bedtime). Correlations were examined between each of the four standard SMBG tests and the mean CGMS values from defined periods that preceded and followed. We also tested how well a low bedtime SMBG predicted nocturnal hypoglycemia. RESULTS: Strong correlations were found between 1) SMBG at breakfast and the mean CGMS value for the preceding 8 h (r = 0.7514), 2) SMBG at dinner and the CGMS from lunch to dinner (r = 0.7538), 3) SMBG at bedtime and the CGMS from dinner to bedtime (r = 0.8145), and 4) SMBG at bedtime and the CGMS from bedtime to breakfast (r = 0.6463). The remaining correlations were weak and not statistically significant. These correlations seem independent of insulin-delivery method as virtually identical results were obtained when data from patients on conventional versus intensive regimes were separately analyzed. A bedtime SMBG <7 mmol/l did not predict nocturnal hypoglycemia (defined as at least one CGMS value <3). CONCLUSIONS: -The breakfast and dinnertime SMBG values are good indicators of integrated glucose values in the time period preceding them, while the bedtime test correlates well with the integrated values both preceding and following it. This information should aid in the meaningful use of SMBG to evaluate glycemic control and make insulin dose adjustments.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".