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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".