Influence of Variables on Hemoglobin A1c Values and Nonheterogeneity of Hemoglobin A1c Reference Ranges
Bibliographic record
Abstract
INTRODUCTION: Hemoglobin A1c (HbA1c) values are influenced by analytical interferences such as HbF and hemoglobin variants and clinical factors such as increased red cell turnover. Although less well-known, demographic factors such as race, age, and sex also influence HbA1c values. The HbA1c reference range should be homogenous in the United States based on the use of National Glycohemoglobin Standardization Program certified methods and the recommendations in the National Academy of Clinical Biochemistry guidelines. METHODS: Data on age, race, sex, HbA1c, and glucose values were extracted from the National Health and Nutrition Examination study for a 3 year period. A search for reference range data for laboratories in the United States was performed using the Google search engine. RESULTS: Extracted data agree with published data on the influence of age, sex, and smoking status on HbA1c values. There is substantial heterogeneity in HbA1c reference ranges in laboratories in the United States. CONCLUSION: Age, sex, and smoking status influence HbA1c values. Despite standardization of HbA1c methods and published recommendations, there is wide heterogeneity in HbA1c reference ranges in the United States.
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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 | 0.015 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".