Antioxidants: Positive or Negative Actors?
Bibliographic record
Abstract
The term "antioxidant" is one of the most confusing definitions in biological/medical sciences. In chemistry, "antioxidant" is simply conceived "a compound that removes reactive species, mainly those oxygen-derived", while in a cell context, the conceptual definition of an antioxidant is poorly understood. Indeed, non-clinically recommended antioxidants are often consumed in large amounts by the global population, based on the belief that cancer, inflammation and degenerative diseases are triggered by high oxygen levels (or reactive oxygen species) and that through blocking reactive species production, organic unbalances/disorders can be prevented and/or even treated. The popularity of these chemicals arises in part from the widespread public mistrust of allopathic medicine. In fact, reactive oxygen species play a dual role in dealing with different disorders, since they may contribute to disease onset and/or progression but may also play a key role in disease prevention. Further, the ability of the most commonly used supplements, such as vitamins C, E, selenium, and herbal supplements to decrease pathologic reactive oxygen species is not clearly established. Hence, the present review aims to provide a nuanced understanding of where current knowledge is and where it should go.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".