Assessment of Anemia Levels in Infants and Children in High Altitude Peru
Bibliographic record
Abstract
When prevalence rates of anemia exceed 40%, the World Health Organization recognizes this as a severe public health problem. In Peru, approximately 43.5% (urban) and 51.1% (rural) of children between the ages of 6 and 36 months have anemia. Currently, limited data exists regarding prevalence rates in many of the high altitude regions of Peru. The main purpose of this pilot study was to establish evidence of anemia in infants and children (7 months through 5 years of age) living in the rural, mountainous region of Ollantaytambo District. This pilot study utilized a quantitative, cross-sectional design to assess the presence of anemia in infants and children. Hemoglobin levels were collected from 160 children across 12 villages where elevations ranged from 2800 to 4100 meters above sea level. Chi Square tests compared anemia with age ranges, altitude, anthropometric measures, breastfeeding patterns, and types of communities. Adjusted hemoglobin levels established 47.5% of the 160 participants were anemic. Chi Square results indicated children aged 25-36 months and children living in communities at 3100 and 4100 meters displayed higher than expected rates of anemia. Results confirmed high rates of anemia and the need for education related to dietary factors.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".