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Record W7141141176 · doi:10.31579/2637-8914/343

Natural Pharmacological Management of Varicose Veins Using Garlic, Onion, and Olive Oil

2025· article· W7141141176 on OpenAlexfundno aff
Rehan Haider *, Zameer Ahmed

Bibliographic record

VenueNutrition and Food Processing · 2025
Typearticle
Language
FieldMedicine
TopicDiagnosis and Treatment of Venous Diseases
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsVasoprotectiveVaricose veinsOlive oilClinical trialDeep fryingAntioxidant

Abstract

fetched live from OpenAlex

Varicose veins are a common vascular disorder caused by venous-valve incompetence and chronic inflammation. Conventional treatments—compression, ablation, and surgery—often alleviate symptoms but may not correct the underlying endothelial dysfunction. Evidence indicates that certain dietary and botanical compounds exert vasoprotective and anti-inflammatory effects that could support venous integrity. This paper reviews the pharmacological potential of garlic (Allium sativum), onion (Allium cepa), and olive oil (Olea europaea). These substances contain sulfur compounds, flavonoids, and phenolic lipids that act on oxidative and inflammatory pathways. Pre-clinical studies show encouraging results, though controlled clinical trials are still needed to define dosage, safety, and efficacy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.331
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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