Advances in methods for characterising dietary patterns: a scoping review
Bibliographic record
Abstract
There is a growing focus on understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterise dietary patterns, such as latent class analysis and machine learning algorithms, may offer opportunities to characterise dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterise dietary patterns. This scoping review synthesised literature from 2005 to 2022 applying methods not traditionally used to characterise dietary patterns, referred to as novel methods. MEDLINE, CINAHL and Scopus were searched using keywords including latent class analysis, machine learning and least absolute shrinkage and selection operator. Of 5274 records identified, 24 met the inclusion criteria. Twelve of twenty-four articles were published since 2020. Studies were conducted across seventeen countries. Nine studies used approaches with applications in machine learning, such as classification models, neural networks and probabilistic graphical models, to identify dietary patterns. The remaining studies applied methods such as latent class analysis, mutual information and treelet transform. Fourteen studies assessed associations between dietary patterns characterised using novel methods and health outcomes, including cancer, cardiovascular disease and asthma. There was wide variation in the methods applied to characterise dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate consistent reporting and enable synthesis to inform policies and programs.
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 | 0.052 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.041 | 0.044 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".