Energy-Dense and Low-Fiber Dietary Pattern May Be a Key Contributor to the Rising Obesity Rates in Brazil
Bibliographic record
Abstract
= 28,153) related to dietary components associated with the risk of obesity. Data from the 2017-2018 Brazilian National Dietary Survey were analyzed. Food consumption was obtained through 24 h recall. Dietary patterns were extracted using partial least squares regression, using energy density (ED), percentage of total fat (%TF), and fiber density (FD) as response variables. In addition, 32 food groups were established as predictor variables in the model. The first dietary pattern, named as energy-dense and low-fiber (ED-LF), included with the positive factor loadings solid fats, breads, added-sugar beverages, fast foods, sauces, pasta, and cheeses, and negative factor loadings rice, beans, vegetables, water, and fruits (≥|0.15|). Higher adherence to the ED-LF dietary pattern was observed for individuals >40 years old from urban areas, in the highest income level, who were not on a diet, reported away-from-home food consumption, and having ≥1 snack/day. The dietary pattern characterized by a low intake of fruits, vegetables, and staple foods and a high intake of fast foods and sugar-sweetened beverages may contribute to the obesity scenario in Brazil.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".