Risk factors, practice variation and hematological outcomes of children identified with non-anemic iron deficiency following screening in primary care setting
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence, risk factors, physician practice patterns and longitudinal hematological outcome of children following screening for non-anemic iron deficiency (NAID). METHODS: The present analysis was a longitudinal cohort study invovling healthy children one to five years of age. Descriptive statistics were used to describe the prevalence, risk factors, practice patterns and hematological outcome of children identified with NAID. The association between NAID and potential risk factors were examined using multivariate logistic regression analysis. RESULTS: Of 2276 children undergoing screening, 155 had NAID, corresponding to a prevalence of 7% (95% CI 5.95% to 8.05%). Risk factors significantly associated with NAID included: younger age (OR 1.08 [95% CI 1.06 to 1.11]), higher body mass index z-score (OR 1.22 [95% CI 1.01 to 1.48]), longer duration of breastfeeding (OR 1.05 [95% CI 1.01 to 1.08]) and increased volume of cow's milk intake (OR 1.13 [95% CI 1.01 to 1.26]). An assessment of practice patterns revealed that for 37% of children, an intervention for NAID was documented; and for 8.4% a physician-ordered follow-up laboratory test was completed to re-evaluate iron status. A total of 58 (37%) children underwent a follow-up laboratory test, of whom 38 (65.5%) had resolution of NAID, 15 (25.9%) had persistence of NAID and two (3.4%) had progression of NAID to anemia. CONCLUSION: NAID is common in early childhood and is associated with modifiable risk factors. Substantial practice variation exists in the management of NAID. Further research is necessary to understand the benefits of screening for NAID and evidence-informed practice guidelines may reduce practice variation in the management of NAID in early childhood.
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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 | 0.004 |
| 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".