Sickle Cell Disease at a Tertiary Care Center in the Vidarbha Region of India: Protocol for a Clinical and Observational Study
Bibliographic record
Abstract
Background: More than 20 million individuals worldwide, especially in the Vidarbha region of India, are affected by sickle cell anemia (SCA), a hereditary condition that results in aberrant hemoglobin S and red blood cell distortion. The condition leads to anemia, organ complications, and recurrent pain crises, making region-specific data necessary for efficient therapy and public health initiatives. Objective: The goal of the study is to examine the clinical characteristics and unusual manifestations of SCA in the Vidarbha region, with an emphasis on dietary practices, clinical presentations, demographic distribution, and lifestyle factors such as alcohol consumption and smoking. Methods: This observational cross-sectional study with random sampling will be conducted at Acharya Vinoba Bhave Rural Hospital in Wardha for 3 months. We will recruit 131 individuals aged 18 to 50 years with dominant hemoglobin S and a positive sickling test. A standardized questionnaire addressing clinical symptoms, nutrition, substance use, inheritance patterns, and demographic information will be used to gather data. SPSS (version 17; IBM Corp) will be used for statistical analysis. Data will be summarized using descriptive statistics. Group differences will be evaluated using inferential tests such as 1-way ANOVA, independent 2-tailed t tests, and chi-square tests. Associations between symptoms and lifestyle variables will be investigated by correlation analysis. Statistical significance is defined as a 2-tailed P value <.05. Results: The anticipated findings may support the need for targeted regional public health initiatives and underscore the importance of comprehensive screening, detailed patient history, and tailored care strategies for individuals with SCA. As of January 2026, this observational study has not received external funding. Participant recruitment and data collection commenced in January 2026 and are currently ongoing. Data analysis will be undertaken following completion of data collection, and the final results are expected to be submitted for publication in April 2026. Conclusions: The findings will support the need for focused regional public health initiatives and emphasize the need for thorough screening, patient history, and customized care techniques for SCA.
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".