Interpretation of A1C measurement in sub-Saharan Africa beyond the global A1C-Derived Average Glucose (ADAG) equation
Bibliographic record
Abstract
Introduction: optimal metabolic control is crucial for prevention of diabetes associated complications. HbA1c is a correlate of chronic hyperglycemia and is associated with long-term diabetes complications. We investigate the relationship between A1C and estimated average blood glucose (eAG) from the multicenter A1C-Derived Average Glucose (ADAG) study, in a sub-Saharan African population. Methods: forty-seven patients with diabetes mellitus and ten normoglycemic individuals were consecutively recruited from a tertiary reference hospital in Cameroon. This observational study was conducted in the framework of the ADAG study. eAG was derived from single values obtained from self-monitored blood glucose (SMBG) and from continuous glucose monitoring (CGM). Spearman correlation coefficient was used to examine the relationship between eAG and A1C levels. Results: there was a strong linear relationship between eAG using SMBG with A1C level; eAG (mmol/l) =1.22 x A1C (%) - 0.25; R2 = 0.58; p<0.001. This suggests that a one percent increase in A1C corresponds to a 1.22 mmol/l increment of eAG. A similar relationship was found between A1C level and eAG from the continuous glucose monitoring (CGM) measurements albeit with a smaller accretion; eAG (mmol/l) =0.95 x A1C (%) + 1.52; R2 = 0.52; p<0.001. The bias of the global ADAG equation was lower than 5% below A1C level of 7% and progressively increased with higher values of A1C. Conclusion: consistent with previous reports, using a population specific equation, A1C can be better derived from eAG in individuals from sub-Saharan African origin.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".