Evaluation of a dual hemoglobin A(2)/A(1c) quantitation kit on the bio-rad variant II automated hemoglobin analyzer.
Bibliographic record
Abstract
CONTEXT: Quantitation of hemoglobin (Hb) A(1c) and investigation of hemoglobinopathy on the Bio-Rad Variant analyzers require a switch between 2 separate kits that is time consuming and causes errors. OBJECTIVE: Evaluation of a new Variant II HbA(2)/HbA(1c) Dual kit capable of both Hb A(1c) quantitation and hemoglobinopathy investigation on a single kit. DESIGN: We evaluated Hb A(1c), Hb A(2), and Hb F quantitation for precision, linearity, and correlation with current methodology. We also evaluated detection of Hb variants and correlation of Hb Barts quantitation. SETTING: Hamilton Regional Laboratory Medicine Program, Provincial Hemoglobinopathy Laboratory, St Joseph's Healthcare Site, Hamilton, Ontario. PATIENTS: Patient blood samples submitted for Hb A(1c) quantitation or hemoglobinopathy investigation. MAIN OUTCOME MEASURES: Precision, linearity, linear regression, and reference interval validation. RESULTS: We provide tables and figures illustrating precision, linearity, linear regression, and quantitation of Hb variants. We validated reference intervals for Hb A(1c), Hb A(2), and Hb F. CONCLUSIONS: The dual kit provides precise Hb A(1c), Hb A(2), and Hb F quantitation. The results show good linearity and correlate well with the results of current methods. We detected all clinically important Hb variants and a wide variety of rare variants. The dual kit has several advantages: it eliminates the need for extensive kit switch over; improves utility for newborn screening because of its quantification of Hb Barts; permits quantification of Hb A(1c) using the beta-Thal method; and eliminates the need for separate Hb A(2) reference intervals for patients with Hb S because of its accurate quantitation of Hb A(2) in the presence of Hb S.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".