Developing a new treatment paradigm for disease prevention and healthy aging
Bibliographic record
Abstract
An increasingly prevalent pattern of risk factors has emerged in middle-aged and older adults that includes the presence of type 2 diabetes or prediabetes, overweight or obese weight status with central obesity and very high body fat, low cardiorespiratory fitness (CRF), low strength, and a low lean-body-mass-to-body-fat ratio. Traditionally, these problems have been approached with a low-fat and low-calorie diet and with lower to moderate intensity activity such as walking. While the treatment has some clear benefits, this approach may no longer be optimal because it does not reflect more recent findings from nutrition and exercise sciences. Specifically, these fields have gained a greater understanding of the metabolic and functional importance of focusing on reducing body fat and central obesity while maintaining or even increasing lean body mass, a quality weight loss, and how to efficiently and effectively increase CRF and strength. Evidence is presented for shifting the treatment paradigm for disease prevention and healthy aging to include the DASH nutrition pattern but with additional protein, higher intensity, brief aerobic training, effort-based, brief resistance training, and structured physical activity. Recent interventions based on social cognitive theory for initiating and then maintaining health behavior changes show the feasibility and efficacy of the approach we are advocating especially within a multiple health behavior change format and the potential for translating the new treatment paradigm into practice.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".