Thalassemia-an untoward situation among the pregnant women in North Bengal district, West Bengal, India
Bibliographic record
Abstract
Background: Thalassemia, an inherited hemoglobin disorder, affects approximately 7% of the global population. In India, thalassemia prevalence ranges from 3-4%, with significant variation among different communities and regions. This study aims to document the prevalence of thalassemia carriers among pregnant women in the Dakshin Dinajpur district of West Bengal, India. Methods: A cross-sectional study was conducted from January 2023 to November 2023. Pregnant women were screened for thalassemia at Block Primary Health Centers, and samples were analyzed at the Thalassemia Control Unit in Balurghat district hospital using High-Performance Liquid Chromatography (HPLC). Results were recorded in Thalamon software and analyzed for carrier rates and demographic correlations. Results: Out of 12,767 pregnant women tested, 29.6% (3,790) were identified as thalassemia carriers. The highest carrier rate was in Kushm and block (45.3%), while the lowest was in Banshihari block (22.9%). Hemoglobin E(Hb-E) carriers constituted 66% of carriers, followed by hemoglobin E disease (21%) and beta thalassemia carriers (9.6%). Significant correlations were found between carrier status and caste, with beta thalassemia being more prevalent among the Scheduled Tribes. Conclusions: The study highlights a high prevalence of thalassemia carriers among pregnant women in Dakshin Dinajpur, particularly Hb-E carriers. Genetic counselling and early screening are crucial to managing and reducing the transmission of thalassemia traits. The findings underscore the need for increased awareness and preventive measures, especially in high-risk communities. Further studies are recommended to develop strategies for reducing maternal complications and preventing carrier transmission.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".