A Systematic Review of Evidence-Based Health Benefits of Oroxylum indicum and Its Functional Food Potential
Bibliographic record
Abstract
Oroxylum indicum is a traditional food in Asia rich in flavonoids and health-related effects. However, research into the use of O. indicum as a functional food ingredient is overlooked. This review synthesized literature from 2010 to 2025 using the PRISMA framework, bibliometric mapping, and the Antecedents–Decisions–Outcomes (ADO) model. In total, 185 articles were included, covering in vitro, in vivo, in silico, and limited human investigations. Bibliometric analysis shows a growing global interest, with recent focuses on molecular docking, cancer, and conservation. Phytochemical investigations consistently report key flavonoids, including baicalein, baicalin, oroxylin A, chrysin, oroxin A, and oroxin B. Studies have linked these compounds to different health benefits, including antioxidants, cardiovascular, and neurological protection. Application of the ADO framework organized research into motives, methods, and outcomes, and highlighted scientifically validated ethnomedicinal uses, such as diabetes and liver protection. Meanwhile, gaps, including obesity-related studies, long-term safety, and clinical trials, remain. More importantly, although young fruits are locally consumed as vegetables or beverages, translation into functional foods is limited by bitterness, lack of standardized preparations, and insufficient dose–response data at dietary intakes. This review discusses the bioactive potential of O. indicum and outlines research priorities for its development in functional food.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".