Ilex Paraguariensis (Yerba Mate) Infusions and Risk of Oral Cancer: A Structured Literature Review
Bibliographic record
Abstract
Background and Objective: Since the emergence of widespread globalization, several food and beverage choices traditionally isolated to a single culture have spread across national/cultural lines. One such export is a traditional South American beverage called mate, an infusion made from the Ilex paraguariensis plant that is consumed widely in Argentina (where it is defined by law as the official national infusion), Uruguay, Paraguay, Brazil, and southern Chile. Recently, infusions of Ilex paraguariensis have become increasingly popular in the United States, Canada and some parts of the Middle East including Lebanon and Syria. Advocates note that it contains antioxidants and a variety of vitamins (2). Furthermore, mate contains a variety of phenolic consituents (4). However, recent research has discovered a possible link between the consumption of mate and oral cancer. As such, the objective of this analysis is to investigate the association between oral cancer and habitual consumption of Ilex paraguariensis infusions. Methods: A structured literature review was conducted using PubMed, Scopus, ScienceDirect and CINAHL. Primary research through 2015 was included; all reviews were excluded. Keywords used were “ilex paraguariensis”, "yerba mate”, “yerba”, “cancer”, “neoplasm”, “neoplasia”, “tumor”, and “tumour”. Articles investigating associations between Ilex paraguariensis infusion consumption and non-oral cancers were excluded.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 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) | 0.005 | 0.000 |
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.
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".