Dietary Strategies for Complementary Feeding between 6 and 24 Months of Age: The Evidence
Bibliographic record
Abstract
Suboptimal complementary feeding practices remain highly prevent. This review aims to comprehensively synthesize new emerging evidence on a set of topics related to the selection and consumption of complementary foods. We synthesized evidence related to five key topics focused on nutritional interventions that target the complementary feeding period, based on four systematic reviews that include updated evidence to February 2022. While there have been many studies examining interventions during the complementary feeding period, there is an overall lack of relevant information through which to draw conclusions on the ideal feeding schedule by food type. Similarly, few studies have examined the effects of animal milk versus infant formula for non-breastfed infants (6-11 months), though those that did found a greater risk of anemia among infants who were provided cow's milk. This review highlights a number of interventions that are successful at improving micronutrient status and anthropometry during the complementary feeding period, including fortified blended foods, locally and commercially produced supplementary foods, and small-quantity lipid-based nutrient supplements. Complementary feeding education for caregivers can also be used to improve nutrition outcomes among infants in both food secure and insecure populations.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".