Application of high performance liquid chromatography in screening abnormal hemoglobinopathy
Bibliographic record
Abstract
Objective To detect the incidence of abnormal hemoglobinopathy with high performance liquid chromatography (HPLC) among people.Methods Between October 2009 and September 2011,6297 inpatients and out-patients received the detections of various abnormal hemoglobins by HPLC,genotypes of deletion-α thalassemia by gap-PCR,genotypes of nondeletion-α thalassemia and β thalassemia by reverse dot blotting (RDB).Results Of 6297 samples,11 types of abnormal hemoglobins(n=68) were found,including Hb E(n=37,notably 2 cases of Hb E/β thalassemia trait double heterozygous),Hb Q (n=10,notably 2 cases of Hb Q combined with Hb H),Hb S(n=3,including 1 case of Hb S homozygous and 2 cases of Hb S heterozygous),Hb D-Iran(n=4),Hb Manitoba (n=4),Hb J-Bangkok (n=5),Hb C (n=1),Hb Lepore (n=1),Hb J-Mexico(n=1),Hb Osu-Christiansborg (n=1) and Hb K(o)ln (n=1).The incidence rate of abnormal hemoglobinopathy was 1.080％ (68/6297).Conclusion HPLC can identify various hemoglobins rapidly and accurately,which is an ideal laboratory tool of screening abnormal hemoglobinopathy. Key words: Chromatography, liquid; Hemoglobins, abnormal; Hemoglobinopathies; Thalassemia
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 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".