Hemoglobin H identification by high‐performance liquid chromatography in confirmed hemoglobin H disease
Bibliographic record
Abstract
INTRODUCTION: Among hemoglobin (Hb) H disease cases diagnosed by DNA testing in our hemoglobinopathy laboratory, we have noted instances of unreported Hb H from high-performance liquid chromatography (HPLC) results of referring laboratories. METHODS: To characterize these issues, we identified all cases of genotypic Hb H disease diagnosed in our laboratory. HPLC chromatograms were reviewed to determine the presence and retention time of the Hb H peak. RESULTS: Hemoglobin H was not reported in 24.2% of patients (23 of 95) with genotypic Hb H disease. The characteristic prerun peak of Hb H was present on review of all eight Variant or Variant II β-thalassemia short-program chromatograms. Elevated Hb F (≥3%) was reported in 14 cases. The Hb H peak was found in the Hb F window in 11 dual program cases. The incorrect identification of Hb H as elevated Hb F resulted in two testing referrals for 'δβ-thalassemia'. CONCLUSIONS: Hemoglobin H may go unreported due to failure to examine for or recognize its peak on Variant or Variant II β-thalassemia short-program chromatograms. Elution of Hb H in the Hb F window resulted in misidentification of Hb H for Hb F and may indicate a Variant II HbA2 /HbA1C program software error. Our findings highlight the need for careful chromatogram inspection and clinical correlation in the diagnosis of Hb H disease.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".