Effectiveness of Ayurvedic Interventions in the Management of Chronic Ulcers: A Protocol for Systematic Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Chronic ulcers—including diabetic foot ulcers, venous leg ulcers, arterial ulcers, and pressure injuries—pose a global health challenge due to prolonged healing, high recurrence, and risk of infection, amputation, and mortality. Conventional wound care technologies have improved outcomes but remain variably effective, particularly in resource-limited settings. In India, Ayurveda continues to be practiced for wound care, with classical texts such as the Sushruta Samhita detailing descriptions of Dushta Vrana (chronic ulcers) and therapeutic modalities including herbal formulations, irrigation, debridement, fumigation, and surgical interventions. While these interventions are widely applied in practice, the evidence base remains fragmented, heterogeneous, and insufficiently synthesized. A systematic review is warranted to critically appraise available literature on Ayurvedic interventions in chronic ulcer care. OBJECTIVE This review aims to systematically identify and synthesize evidence on Ayurveda-based interventions for chronic ulcers, focusing on (1) diagnostic criteria, (2) assessment parameters, (3) treatment rationale, (4) topical and systemic therapies used, and (5) reported outcomes. METHODS We will include randomized controlled trials, non-randomized studies, and case reports involving human participants with chronic ulcers of any etiology. Interventions include Ayurveda-based topical or systemic therapies, alone or in combination with other treatments. Primary outcome is wound healing; secondary outcomes include healing rate, unit healing time, epithelialization, neovascularization, antimicrobial activity, and recurrence. Databases searched will include PubMed, Embase, CENTRAL, Scopus, AYUSH Research Portal, DHARA, and Shodhganga, alongside grey literature. Risk of bias will be assessed using RoB 2 for RCTs and ROBINS-I for non-randomized studies. Data synthesis will follow PRISMA 2020 guidelines, with meta-analysis where feasible and narrative synthesis otherwise. RESULTS As this is a protocol, results are not yet available. CONCLUSIONS This systematic review will provide a consolidated evaluation of Ayurvedic interventions in chronic ulcer management, identify research gaps, and inform the development of evidence-based integrative wound care strategies. CLINICALTRIAL PROSPERO registration No- CRD420251077147
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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 | 0.045 | 0.075 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.091 | 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".