Food insecurity is related to obesity and lipid alterations in Mexican college students (805.2)
Bibliographic record
Abstract
ELCSA (Encuesta Latinoamericana y del Caribe sobre Seguridad Alimentaria) has been validated and used in México for estimating Food Insecurity (FI). The objective of this study was to assess food insecurity in students from the Autonomous University of Queretaro, in México. A total of 662 students answered the ELCSA questionnaire, a food frequency questionnaire in addition, to a nutritional assessment based on weight, height, waist circumference, body composition and biochemical indexes including anemia, glucose, triglycerides and cholesterol. The 50.5% of students had some degree of FI, mainly emphasizing the mild (39.4 %). A higher prevalence of overweight and obesity were observed in students reporting severe‐FI than in those with mild and moderate FI. In contrast , the percentage of students with high body fat decreased with increasing severity of FI , even if the average value of % BF was not statistically different between IF‐students with mild and moderate (24.9 ±7.5 vs. 25.0±7.5, P > 0.05). Anemia affected 1.1% of students, being more prevalent the micro and macrocytic types in those students with FI. Severe FI‐students had the greatest number of cases with hypertriglyceridemia (33.3 %) and hypercholesterolemia (16.7%). According to the findings in this study, FI in Mexican college students could be an important factor related to the occurrence of overweight and obesity and its associated comorbidities among which include hypertriglyceridemia and hypercholesterolemia. Grant Funding Source : Supported by the Autonomus University of Queretaro
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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 | 0.000 | 0.001 |
| 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".