Targets and tactics: the relative importance of HbA1c, fasting and postprandial plasma glucose levels to glycaemic control in type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: The incidence of type 2 diabetes is reaching pandemic proportions, impacting patients and healthcare systems across the globe. Evidence suggests that a majority of patients are not achieving recommended blood glucose targets resulting in an increased risk of micro- and macro-vascular complications. AIM: To review literature on the significance of glycosylated haemoglobin (HbA(1c)), fasting plasma glucose (FPG) and postprandial plasma glucose (PPG), their inter-relationships and relative importance in the treatment of diabetes, and to provide practical guidance on effective monitoring of patients. METHODS: Clinical guidelines on diabetes management and clinical and preclinical studies of glycaemic control identified through a publications database search were reviewed. RESULTS: Glycaemic control remains fundamental to the successful management of diabetes. HbA(1c) is the gold standard measure of glycaemic control but recent evidence suggests that postmeal hyperglycaemia also plays an important role in the aetiology of diabetes-associated complications and control of PPG levels is vital to the achievement of recommended HbA(1c) targets. CONCLUSIONS: The call for action on type 2 diabetes has never been more compelling; with a clear focus on strategies for glycaemic control, the impact of the diabetes pandemic can be limited.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".