Research strategies in nutrition in health and disease: The failure of mechanistic research
Bibliographic record
Abstract
This paper critically evaluates different research methods in order to assess their value for establishing which dietary changes are most effective for protecting health and preventing disease. The evidence demonstrates that the combined use of observational studies (mainly cohort studies) and randomized controlled trials (RCTs) is the most successful strategy. Studies of the details of body mechanisms in health and disease (mechanistic research) is another commonly used research strategy. However, much evidence demonstrates that it is a far less successful strategy. In order to support the above conclusions research studies from the following areas are discussed: obesity and dietary fat; heart disease and saturated fat; the Mediterranean diet and cardiovascular disease; type 2 diabetes and dietary fiber; and cancer and micronutrients. While mechanistic research has a poor track record in nutrition, it has achieved some success in other areas of biomedical science. This is shown by examining the role of mechanistic research in the discovery of new drugs.
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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 | 0.423 | 0.337 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.018 | 0.034 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".