Categorizing ultra-processed food intake in large-scale cohort studies: evidence from the Nurses’ Health Studies, the Health Professionals Follow-up Study, and the Growing Up Today Study
Bibliographic record
Abstract
Abstract Objective There is limited description and documentation of the methods used for the categorization of dietary intake according to the NOVA classification, in large-scale cohort studies. This manuscript details the strategy employed for categorizing the food intake, assessed using food frequency questionnaires (FFQs), of participants in the Nurses’ Health Studies (NHS) I and II, the Health Professionals Follow-up Study (HPFS), and the Growing Up Today Studies (GUTS) I and II into the four NOVA groups to identify the ultra-processed portion of their diets. Methods A four-stage approach was employed: (1) compilation of all food items from the FFQs used at different waves of data collection; (2) assignment of food items to a NOVA group by three researchers working independently; (3) checking for consensus in categorization and shortlisting food items for which there was disagreement; (4) discussions with experts and use of additional resources (research dieticians, cohort-specific documents, online grocery store scans) to guide the final categorization of the short-listed items. Results At stage 1, 205 and 315 food items were compiled from the adult and GUTS FFQ food lists, respectively. Over 70% of food items from all cohorts were assigned to a NOVA group after stage 2 and the remainder were shortlisted for further discussion (stage 3). Two rounds of reviews at stage 4 helped with the categorization of 96.5% of items from the adult cohorts and 90.7% items from the youth cohort. The remaining products were assigned to a non-ultra-processed food group and ear-marked for sensitivity analyses. Of all items in the food lists, 36.1% in the adult cohorts and 43.5% in the GUTS cohorts were identified as ultra-processed. Conclusion An iterative, conservative approach was used to categorize food items from the NHS, HPFS and GUTS FFQ food lists according to their grade of processing. The approach relied on discussions with experts and was informed by insights from the research dieticians, information provided by cohort-specific documents, and scans of online supermarkets. Future work is needed to validate this approach.
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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 | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".