Bibliographic record
Abstract
One of the problems which may beset epidemiological studies is the difficulty of accurately measuring the dietary intakes of participants. Biomarkers of diet promise to provide a more accurate measure of dietary intake and a more objective one in that they are not reliant on the subject's memory. This review considers some issues of importance in epidemiology when information is obtained from biomarkers. The approach taken is to use examples both of normal dietary constituents and of contaminants in relation to a range of diet and health questions to illustrate these points. A brief overview of the role of sample collection, processing and storage is given including some generic recommendations for maximising the reliability of subsequent analytical data. Using the examples of phytoestrogens and iodine the question of whether biomarkers can accurately reflect the intake of the dietary constituents of interest at the population level or at the individual level is considered. The relationship of the biomarker to the natural history of the disease is exemplified using the role of folate in neural tube defects. Finally, intakes of vitamin D and heterocyclic amines are used to illustrate the integration of biomarkers into epidemiological studies of prostate and colorectal cancer, respectively. It is concluded that biomarkers may provide a more accurate and objective measure of diet than estimates of current or usual intake but that this approach also has limitations. A combination of methods will probably prove to be most valuable and this approach is being taken in current large prospective studies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".