Iron deficiency in young children: Surveillance versus screening?
Bibliographic record
Abstract
Parkin and Borkhoff are commended on their outstanding “Practical tips for paediatricians: Assessment and management of young children with iron deficiency” (1). Iron deficiency (ID), the most common nutritional deficiency, is a significant and underestimated Canadian public health problem (2). Parkin and Borkhoff identify important practical tips, including key risk factors for ID. However, other risk factors may include maternal anemia, hypertension, and gestational diabetes. Notably, the authors do not address surveillance (testing based on identification of risk factors) versus screening (testing of all young children) in order to identify ID in early childhood. In 1998, the Canadian Task Force on the Periodic Health Examination (Grade B—Fair evidence to recommend the clinical preventive action) recommended surveillance for infants of low socioeconomic status, of Chinese ethnicity, of Aboriginal ancestry, of low birth weight or fed whole cow milk during the first year of life (3). The US Preventive Services Task Force, supported by the American Academy of Family Physicians, found inadequate evidence of benefit on growth or child cognitive, psychomotor, or neurodevelopmental outcomes to justify routine screening for iron deficiency anemia in asymptomatic children aged 6 to 24 months (4,5). In keeping with this, the Centers for Diseases Control and Prevention recommends surveillance at 9 to 12 months, 6 months later and at 2 to 5 years of age only in children who are at high risk for iron deficiency anemia (6). In contrast, the American Academy of Pediatrics recommends universal screening at 12 months of age and continued surveillance for children who are at increased risk for ID (7). Further, the United States Institute of Medicine recommends surveillance at 9 months for breastfeeding infants and re-testing at 15 to 18 months for those with ID (8).
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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 | 0.008 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".