Molecular Epidemiology of Hemoglobinopathies in Cambodia
Bibliographic record
Abstract
Determining the magnitude of the thalassemia problem in a country is important for implementing a national prevention and control program. In order to acquire accurate thalassemia prevalence data, the gene frequency of α- and β-thalassemia (α- and β-thal) in different regions of a country should be determined. The molecular basis of thalassemia in Cambodia was performed by polymerase chain reaction (PCR)-based techniques in a community-based cross-sectional survey of 1631 unrelated individuals from three regions, Battambang, Preah Vihear and Phnom Penh. Thalassemia mutations were detected in 62.7% of the three studied population of Cambodia. Hb E (HBB: c.79G > A) was the most common β-globin gene mutation with a frequency ranging from 0.139 to 0.331, while the most frequent α-globin gene mutation was the -α(3.7) (rightward) deletion (0.098-0.255). The other frequencies were 0.001-0.003 for β-thal, 0.008-0.011 for α-thal-1 (- -(SEA)), 0.003-0.008 for α-thal-2 [-α(4.2) (leftward deletion)], 0.021-0.044 for Hb Constant Spring (Hb CS, HBA2: c.427T > C) and 0.009-0.036 for Hb Paksé (HBA2: c.429A > T). A regional specific thalassemia gene frequency was observed. Preah Vihear had the highest prevalence of Hb E (55.9%), α-thal-2 (24.0%) and nondeletional α-thal (15.1%), whereas Phnom Penh had the lowest frequency of thalassemia genes. Interestingly, in Preah Vihear, the frequency of Hb Paksé was extremely high (0.036), almost equivalent to that of Hb CS (0.044). Our results indicate the importance of micromapping and epidemiology studies of thalassemia, which will assist in establishing the national prevention and control program in Cambodia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".