Behavioural treatments for chronic systemic inflammation: effects of dietary weight loss and exercise training
Bibliographic record
Abstract
Persistent low-grade inflammation, as indicated by higher circulating levels of inflammatory mediators such as C-reactive protein, interleukin-6 and tumour necrosis factor-alpha, is a strong risk factor for several chronic diseases. There are data indicating that decreasing energy intake and increasing physical activity may be effective therapies for reducing overall inflammation. Evidence is strong that circulating levels of inflammatory markers are elevated with total and abdominal obesity, possibly owing to a higher secretion rate of cytokines by adipose tissue in obese people. Moreover, very-low-energy dietary weight loss reduces both circulating markers of inflammation and adipose-tissue cytokine production. Data from several large population-based cohorts show an inverse association between markers of systemic inflammation and physical activity or fitness status; small-scale intervention studies support that exercise training diminishes inflammation. Dietary weight loss plus exercise is likely more effective than weight reduction alone in reducing inflammation. To date, data from randomized, controlled trails designed to definitively test the effects of weight loss or exercise training, or both, on inflammation are limited. Future studies are required to define the amount of weight loss needed for clinically meaningful reductions of inflammation; in addition, fully powered and controlled studies are necessary to clarify the effect of exercise training on chronic, systemic inflammation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".