Meta-Analysis and Systematic Review of Micro- and Macro-Nutrient Intakes and Trajectories of Macro-Nutrient Supply in the Eastern Mediterranean Region
Bibliographic record
Abstract
The Eastern Mediterranean Region (EMR) is experiencing a nutrition transition, characterized by the emergence of overnutrition and micro-nutrient deficiencies. No previous study has comparatively examined nutrient intake in adults across countries in the EMR. This review examined the adequacy of nutrients in adults living in the EMR. Moreover, it analyzed the food balance sheets (FBS) for 1961–2018 to identify the trajectory of energy supply from macro-nutrients in the EMR. A systematic search was conducted from January 2012 to September 2020. Only observational studies were retained with a random sampling design. An assessment of the methodological quality was conducted. Levels of nutrient daily intake and their adequacy compared to the daily reference intake of the Institute of Medicine were reported across the region. No studies were identified for half of the region’s countries. Although nutrient energy intake was satisfactory overall, fat and carbohydrate intake were high. Intake of vitamin D, calcium, potassium, zinc, and magnesium were below that recommended. The analysis of the FBS data allowed for the identification of four linear patterns of trajectories, with countries in the EMR best fitting the ‘high-energy-supply from carbohydrate’ group. This systematic review warrants multi-sectorial commitment to optimize nutrient intake.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".