Bibliographic record
Abstract
Dietary fibres consist of a heterogeneous group of carbohydrate polymers which resist digestion by human gastrointestinal enzymes. Consumption of dietary fibre has been linked with innumerable health benefits encompassing the foundational pillars of metabolic health from obesity to hypertension, dyslipidaemia, and type 2 diabetes mellitus (T2DM), with many of these benefits linked with metabolites produced by the fermentation of fibre by gut microbes [1]. The potential of dietary fibre to provide a safe and effective nonpharmacologic complementary tool with which to combat the evolving consequences of the obesity epidemic has recently garnered tremendous attention in both medical literature and lay media alike. HIGHLIGHTS: Consumption of dietary fibre has been linked with innumerable health benefits encompassing the foundational pillars of metabolic health with many of these benefits linked with metabolites produced by the fermentation of fibre by gut microbes At the present time clinicians are faced with an impossible task in which there is rapidly mounting evidence for dietary fibres advancing metabolic health, but little practical options for healthcare providers other than to simply recommend patients consume more fibre. Benefits of fibre intake may perhaps be maximised in an individual by matching specific fibre consumption with existing microbial functional characteristics If dietary fibres could be demonstrated to act as successful adjuncts to sustain or improve standard of care therapies or even alleviate common gastrointestinal side effects associated with current treatments, they would be an invaluable tool in our metabolic health armamentarium. Neither Dr. Madsen or I have any financial conflicts of interest pertinent to the contents of this manuscript.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".