Integrative medicine and human health ‐ the role of pre‐, pro‐ and synbiotics
Bibliographic record
Abstract
Western lifestyle is associated with a sustained low grade increase in inflammation -increased levels of endotoxin in the body and increased activation of Toll-like receptors and neutrophils, which leads to impaired immunity and reduced resistance to disease, changes which might explain the epidemic of chronic diseases spreading around the globe. The immune system cannot function properly without access to bacteria and raw plants, rich not only in bacteria but also in plant fibre, antioxidants, healthy fats and numerous other nutrients. Modern food technology with plant breeding, separation, condensation of food ingredients, heating, freezing, drying, irradiation, microwaving, are effective tool to counteract optimal immune function, and suspected to be a leading cause of so called Western diseases. Supply of pre-, pro-, and synbiotics have sometimes proved to be effective tools to counteract, especially acute diseases, but have often failed, especially in chronic diseases. Thousands of factors contribute to unhealth and numerous alterations in life style and food habits are often needed, in order to prevent and cure "treatment-resistant" chronic diseases. Such alterations include avoiding processed foods rich in pro-inflammatory molecules, but also a focus on consuming substantial amounts of foods with documented anti-inflammatory effects, often raw and fresh green vegetables and tubers such as turmeric/curcumin.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".