Impact of Changing Eicosapentaenoic Acid and Docosahexaenoic Acid Levels on Hematologic Biomarkers
Bibliographic record
Abstract
Objectives: The objectives of the study were to assess inflammation and immune health in children aged 5 to 9 years living in a low-income environment and to determine the association of measures of immune status with child BMI-for-age.Methods: This cross-sectional study is part of a larger study investigating dietary intake, cognition, behavior, and physical fitness in young schoolchildren.A total of 158 apparently healthy children (i.e., no known or current co-morbidities e.g.fever, cough etc. and actively attending class) in kindergarten and first grade were randomly selected from two public schools in Metro Manila, Philippines.Venous blood was extracted and immune health determined from a complete blood count.Serum C-reactive protein was determined by standard CRP test.Weight and height were measured using a standard Detecto weighing scale with height rod.The 2007 WHO Growth Reference (BMI-for-age) was used for classification.Chi-square test and Fisher's exact test (SPSS v.26) were used to assess differences between groups.Results: Inflammation (measured as CRP) was more prevalent among children with high BMI (overweight and obese) (31.3%) than among those with normal and low BMI (6.8% and 4.0%, respectively) (p.001).Neutropenia (low neutrophils) was more prevalent among children with high BMI (58.1%) than children with normal (54.3%) or low BMI (18.2%) (p.011).High eosinophil level was more prevalent among children with low BMI (45.5%) compared with normal (21%) and high BMI (19.4%) (p.04).Conclusions: Children in this sample with overweight and obese BMI-for-age were more likely to have inflammation and poorer immune health (characterized by low neutrophils) than those with either normal or low BMI.High eosinophil levels among low BMI (thin) children could possibly be due to the presence of parasitic infection, as these children often belonged to the poorest families in the community whose access to sanitation facilities were inadequate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".