Do Postprandial Glucose Levels add Important Clinical Information When Fasting Glucose Levels are Near Normal in Non-Insulin-Dependent Patients with Type 2 Diabetes?
Bibliographic record
Abstract
Background: Practice guidelines recommend that both fasting and postprandial blood glucose measurements be performed to achieve glycemic targets, yet few type 2 diabetes patients engage in postprandial glucose (PPG) testing. The purpose of this study was to determine if PPG testing provides important additional clinical information beyond fasting plasma glucose (FPG) tests in well-controlled, non-insulin-dependent type 2 diabetes patients. Methods: Subjects were recruited from 8 pharmacies and instructed to perform daily FPG tests during days 1 to 7 (run-in phase) and daily FPG and PPG tests during days 8 to 21 (test phase). Results: The mean FPG from 362 tests ( n = 52 subjects) in the run-in phase was 7 mmol/L (SD 1.4). In the test phase, the mean FPG was 7 mmol/L (SD 1.6) and the mean PPG was 8.4 mmol/L (SD 2.2) from 700 tests. For FPG tests in the recommended target range of 4 to 7 mmol/L, 87% (322/370) of corresponding (same-day) PPG tests were within the target range of 5 to 10 mmol/L. In subjects whose mean FPG was 4 to 7 mmol/L, 87% of PPG tests were also within target limits. Conclusion: Community pharmacists are often asked by patients how frequently they should be monitoring their blood glucose, but the evidence supporting self-monitoring of blood glucose (SMBG) in non-insulin-dependent type 2 diabetes patients is conflicting and unclear. Given the results from this small study, testing PPG may be unnecessary for non-insulin-dependent type 2 diabetes patients achieving FPG targets, but further study is required.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".