Bibliographic record
Abstract
The objectives of this research were to determine the prevalence of anemia, and identify risk factors for anemia, in 9-month-old Cree infants living in northern Quebec. The prevalence of anemia (hemoglobin <110 g/L) was 25--32%, depending on the study sample. Iron deficiency was present in 28.2% of infants who could be classified and 14.4% had iron deficiency anemia. Fewer than 2% of infants had low birth weight (<2500 g) so most infants should have been born with adequate iron stores. One cause of anemia that was identified was a diet that was low in iron. Only 15.1% of infants were reported by guardians to eat meat daily and 28.5% were reported to never eat meat. Infants who were breastfed or cow's milk fed did not obtain sufficient iron for effective erythropoiesis. Compared with formula that was predominantly iron fortified, the odds ratio (OR) for anemia was 7.9 (95% CI 3.4--18.2) for breast milk and 5.0 (95% CI 2.0--12.7) for cow's milk. When milk type was controlled for, weight gain since birth was significantly associated with microcytic erythrocytes (OR comparing the highest tertile of weight gain to the lowest tertile 2.9, 95% CI 1.2--6.6). This indicates that fast-growing infants were not meeting their iron needs for growth. Another risk factor for anemia that was identified was common childhood infections. The prevalence of anemia among infants reported as recently unwell with an infection was higher than among infants reported as recently well (31.1% vs. 19.0%, chi2 = 4.27, p = 0.039). The prevalence of elevated blood lead was 2.7% and is not a major public health problem. No evidence for vitamin A deficiency was found. Serum retinol was positively associated with all iron status indicators. Cree infants who were given supplements containing vitamin A had a lower prevalence of anemia (hemoglobin <105 g/L) (10.8% vs 23.2%, chi2 = 5.97, p = 0.015). These results suggest a role for vitamin A in iron metabolism. To prevent anemia in aboriginal i
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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 | 0.000 |
| 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".