Bibliographic record
Abstract
Four categories of foods are proposed in the NOVA food classification, which seeks to relate food processing as the primary driver of diet quality. Of these, the category "ultra-processed foods" has been widely studied in relation both to diet quality and to risk factors for noncommunicable disease. The present paper explores the definition of ultra-processed foods since its inception and clearly shows that the definition of such foods has varied considerably. Because of the difficulty of interpretation of the primary definition, the NOVA group and others have set out lists of examples of foods that fall under the category of ultra-processed foods. The present manuscript demonstrates that since the inception of the NOVA classification of foods, these examples of foods to which this category applies have varied considerably. Thus, there is little consistency either in the definition of ultra-processed foods or in examples of foods within this category. The public health nutrition advice of NOVA is that ultra-processed foods should be avoided to achieve improvements in nutrient intakes with an emphasis on fat, sugar, and salt. The present manuscript demonstrates that the published data for the United States, United Kingdom, France, Brazil, and Canada all show that across quintiles of intake of ultra-processed foods, nutritionally meaningful changes are seen for sugars and fiber but not for total fat, saturated fat, and sodium. Moreover, 2 national surveys in the United Kingdom and France fail to show any link between body mass index and consumption of ultra-processed foods. The paper concludes that constructive scholarly debate needs to be facilitated on many issues that would be affected by a policy to avoid ultra-processed foods.
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 | 0.066 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.026 | 0.049 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.031 | 0.036 |
| 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".