Effectiveness of Herbal Interventions in the Management of Hypercholesterolemia: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Hypercholesterolemia is a significant risk factor for cardiovascular diseases, necessitating effective management strategies. Herbal interventions have gained attention as potential alternative or complementary therapies to conventional lipid-lowering medications. OBJECTIVE: This systematic review aims to evaluate the effectiveness of various herbal interventions in managing hypercholesterolemia. METHODS: A comprehensive literature search will be conducted across multiple databases, including PubMed, MEDLINE, EMBASE, EBSCO, Cochrane Library, DHARA, AYUSH research portal, WHO portal, Shodhganga, and Google scholar as well as dissertations and thesis work available in the public domain and unpublished works from university websites for studies published from January 2020 onward. Randomized controlled trials investigating the impact of herbal interventions on cholesterol levels will be included. The primary outcome is the change in low-density lipoprotein cholesterol and high-density lipoprotein cholesterol levels. Data extraction and quality assessment will be performed independently by 3 reviewers, and discrepancies will be resolved by a fourth reviewer. All statistical analyses will be conducted using the Cochrane Collaboration software program, RevMan Web. RESULTS: Following an initial screening, 96 studies were identified. After removing duplicates, 7 studies were excluded, leaving 89 studies ready for data extraction. The results of these studies will be synthesized and analyzed upon completion. CONCLUSIONS: This review aims to synthesize evidence on the potential benefits of herbal interventions in managing hypercholesterolemia. Preliminary findings suggest that specific herbal interventions may contribute to lower cholesterol levels, potentially complementing standard hypercholesterolemia management strategies. The findings will be systematically analyzed and presented upon completion of the review, providing insights into the effectiveness of integrating these interventions into current treatment protocols. TRIAL REGISTRATION: PROSPERO CRD42024548858; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024548858. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68016.
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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 | 0.065 | 0.067 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.020 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.073 | 0.009 |
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".