Bibliographic record
Abstract
This paper makes a critical evaluation of several of the research methods used to investigate the relationship between diet, health, and disease. The two widely used methods are randomized controlled trials (RCTs) and prospective cohort studies. RCTs are widely viewed as being more reliable than cohort studies and for that reason are placed higher in the research hierarchy. However, RCTs have inherent flaws and, consequently, they may generate findings that are less reliable than those from cohort studies. The text presents a discussion of the errors that may occur as a result of confounding. This refers to the correlation of the exposure and the outcome with other variables and can mask the true association or produce false associations. Another source of error is reverse causation, which is most commonly associated with cross-sectional studies. These studies do not allow researchers to determine the temporal sequence of lifestyle and other inputs together with health-related outcomes. As a result, it may be unclear which is cause and which is effect. This may also occur with cohort studies and can be illustrated by the inverse association between alcohol intake and coronary heart disease. Mechanistic research refers to the investigation of the intricate details of body functioning in health and disease and this research strategy is widely used in biomedical science. The evidence presented here makes the case that most of our information of practical value in the field of nutrition and disease has come from epidemiological research, including RCTs, whereas mechanistic research has been of minor value.
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 | 0.447 | 0.552 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.011 | 0.081 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.018 | 0.045 |
| 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; 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".