Diet and health: the need for new and reliable approaches
Bibliographic record
Abstract
This editorial refers to ‘The associations of major foods and fibre with risks of ischaemic and haemorrhagic stroke: a prospective study of 418 329 participants in the EPIC cohort across nine European countries’†, by T.Y.N. Tong et al., on page 2632. Nutrition has long been related to health. Until the mid-1950s, the predominant concern in most countries was undernutrition but, since the 1960s, the focus has shifted towards the potential harmful effects of excess consumption of certain foods, especially animal fats.1 Given the large increases in cardiovascular disease (CVD) in the mid- and late 1950s in Western countries, the focus has been on CVD with specific emphasis on coronary heart disease (CHD). However, apart from the apparent protective effects of fruit and vegetables for stroke or CHD, the associations of other aspects of diet with CVD have been inconsistent. Why is this? Since the 1960s, conventional thinking on nutrition and CHD was fuelled by epidemiological studies such as the Seven Countries Study.2 Although influential, this study had significant methodological problems including selection biases, limited data on diet (recorded in only ∼5% of the sample), using non-standardized and non-validated methods, and inconsistent methods of follow-up. Subsequently better designed large prospective cohort studies were initiated, mostly in the USA and Europe. Given that in these countries, diseases of ‘undernutrition’ (e.g. vitamin deficiencies or protein calorie malnutrition) were uncommon by the 1960s, the focus was largely on whether overconsumption of specific foods such as animal fats (e.g. red meat which contains saturated fats, or eggs, a major source of dietary cholesterol) was harmful. However, these studies ignored the fact that some of these foods also contained potentially beneficial nutrients such as high-quality proteins, minerals, folate, vitamin D, vitamin Bs, and monounsaturated fats. Nevertheless, because of ‘theoretical’ concerns that these foods (as well as dairy products) may adversely affect blood lipids, guidelines have recommended limiting meat, eggs, or dairy (especially whole fat) consumption. However, these recommendations did not consider that there may be a critical need for many natural foods and that a minimal amount of intake may be needed.
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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 | 0.035 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.076 | 0.105 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".