Effect of Hemoglobin Variants (Hb J, Hb G, and Hb E) on HbA1c Values as Measured by Cation-Exchange HPLC (Diamat)
Bibliographic record
Abstract
Hemoglobin A1c (HbA1c) is used for the long-term management of patients with diabetes mellitus (DM) (1)(2). Hb variants other than HbA1c and ε-N-lysine-glycated Hb A0 may cause analytical interference in determinations of HbA1c (3)(4)(5)(6). In one study, the authors estimated the prevalence of thalassemia in Taiwan as 7%; moreover, ∼1% of the people in northern Taiwan are β-thalassemia heterozygotes (7). The occurrence of 24 abnormal Hbs (13 α-chain variants and 11 β-chain variants), including Hb G-Taipei, in populations in the Silk Road area of Northwestern China has been presented in a review (8). The frequency of thalassemia has been estimated to be ∼1 in 2350 in Japan (9) and even higher in North Africa (10). Hb E is the second most prevalent Hb variant worldwide and the third most prevalent variant in the US, after Hb S and C. Hb E is found primarily in Southeast Asia, especially among the Thai population (11). In the northeastern region of India, the gene frequency of Hb E is 10.9% (12). In a study of 222 000 blood samples in Canada, 23 cases of Hb J were identified (13). Given that the majority of hemoglobinopathic cases are from families of Asian, Southeast Asian, and Asian Indian ancestry (7)(8)(9)(10)(11)(12)(14)(15)(16), the aim of this study was to investigate the influence of selected Hb structural variants on HbA1c values measured by cation-exchange HPLC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".