Assessment of complementary feeding of Canadian infants: effects on microbiome & oxidative stress, a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: The World Health Organization recommends exclusive breastfeeding until 6 months followed by introduction of iron-rich complementary foods (CFs). The aim of this study was to determine the impact of different iron-rich CFs on infant gut inflammation and microbiota. METHODS: Eighty-seven exclusively breastfed infants were randomly assigned to receive one of the following as their first CF: iron-fortified cereal (Cer), iron-fortified cereal with fruit (Cer + Fr), or meat (M). Urine and stool samples were collected to assess reactive oxygen species (ROS) generation, gut microbiota and inflammation. RESULTS: Fecal iron differed across feeding groups (p < 0.001); levels were highest in the Cer group and lowest in M group. A significant increase of fecal ROS formation (p < 0.002) after the introduction of CFs was observed, but did not differ across feeding groups. Fecal calprotectin increased within all groups after the introduction of CFs (p = 0.004). Gut microbiota richness increased after introduction of M or Cer + Fr. Regardless of feeding group, Coriobacteriaceae were positively correlated with ROS and Staphylococcaceae were negatively correlated with calprotectin. CONCLUSIONS: Choice of first CF may influence gut inflammation and microbiota, potentially due to variations in iron absorption from different foods. Further research is warranted to fully characterize these associations and to establish implications for infant health. This study was registered in the ClinicalTrial.gov registry (Identifier No. NCT01790542 ). TRIAL REGISTRATION: This study was registered in the ClinicalTrial.gov registry under the name "Assessment of Complementary Feeding of Canadian Infants" (Identifier No. NCT01790542 ) February 6, 2013.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".