Efficacy of an Ayurvedic Intervention as an Adjunct to Standard Care in Preventing Acute Pain Crises in Sickle Cell Anemia: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
Background: Sickle cell anemia (SCA) represents a major health concern among the tribal population of India, with frequent acute pain crises significantly compromising the quality of life of the affected individuals. As an inherited disorder, there is no definitive cure for the condition. Hydroxyurea remains the primary therapeutic option and is typically used for lifelong management, although it may be associated with certain side effects. In light of the pressing need for a safe, accessible, and effective alternative for long-term care, this study is planned to explore the potential of Ayurveda in managing pain crises alongside conventional standard care. Objective: This study aims to evaluate the efficacy of Ayurvedic intervention in preventing acute pain crises in individuals with SCA and improving their quality of life. Methods: This study is designed as a randomized, active-controlled, open-label clinical trial. Participants diagnosed with SCA are enrolled in the study according to the selection criteria. The intervention group receives Ayurvedic interventions, namely dadimadi ghrita and Ayush-RP, along with standard care, whereas the control group receives standard care only. The intervention is administered over a period of 8 months. Participants are evaluated on the 30th, 60th, 105th, 150th, 195th, and 240th days to assess changes in the frequency of pain crises and quality of life. Results: This study was initiated on September 5, 2023. Total enrollment of 1510 participants has been completed by screening 1644 participants. As of August 14, 2025, a total of 1137 participants have successfully completed the study, 280 (24.62%) are continuing, and 93 (8.18%) have dropped out. Conclusions: This study aims to establish the efficacy and safety of Ayurvedic interventions as part of an integrated approach to managing SCA, with a focus on reducing the frequency of pain crises and improving patients' overall quality of life.
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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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.008 |
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".