Maternal and cord blood parameters are associated with placental and newborn outcomes in indigenous mothers: A case study in the MINDI cohort
Bibliographic record
Abstract
Background: Multiple infections, nutrient deficiencies and inflammation (MINDI) occur in indigenous communities, but their associations with perinatal outcomes have not been described. Objective: To assess maternal and cord blood micronutrient and inflammation status in peripartum mothers from the Ngäbe-Buglé comarca in Panama, and their associations with placental and infant outcomes. Methods: In 34 mother-newborn dyads, placental weight and diameter were measured, and maternal and cord blood were processed for complete cell counts, serum C-reactive protein, ferritin, serum transferrin receptor (sTfR), vitamins A and D. Blood volumes were calculated using Nadler's formula. Results: Mothers had low plasma volume (<2.8 L, 96%), vitamin A (52.9%), vitamin D (29.4%), iron (58.8%) and hemoglobin (23.5%), but high hematocrit (>40%, 17.6%) and inflammation (C-reactive protein >8.1 mg/L, 85.3%). Birthweights were normal, but low placental weight (35.3%), low head circumference Z-scores (17.6%), and low cord hemoglobin (5.9%), iron (79.4%), vitamin A (14.7%) and vitamin D (82.3%) were identified. Maternal and cord vitamin D were highly correlated. Higher maternal plasma volume was associated with heavier placentae (β= 0.57), and higher cord D (β= 0.43) and eosinophils (β= 0.43) with larger placentae. Hemoconcentration (higher cord hematocrit) was associated with lower newborn weight (β= -0.48) and head circumference (β= -0.56). Inflammation [higher maternal neutrophils (β= -0.50), and cord platelets (β= -0.32)] was associated with lower newborn length and head circumference. Conclusion: Maternal-newborn hemoconcentration, subclinical inflammation and multiple nutrient deficiencies, particularly neonatal vitamin D deficiency, were identified as potential targets for interventions to improve pregnancy outcomes in vulnerable communities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".