The correlation between anemia and intelligence quotient (IQ) in children: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Anemia is considered as an important health problem because it severely affects children's growth and development. It impairs the immune mechanisms and is also associated with increased morbidity. This meta-analysis aims to assess the correlation between anemia and intelligence quotient (IQ) in children. Methods: Articles on anemia and IQ in children under 18 years old were searched in Scopus, Pubmed, and ScienceDirect, using the search term “(((Anemia) OR (Hemoglobin)) AND ((Intelligence Quotient) OR (IQ)))”. Articles before 2000 and published in languages other than English were excluded. The outcome is Intelligence Quotient. Two independent reviewers performed article screening. The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). The statistical analysis was conducted using Review Manager 5.4. Results: A total of 7 published studies with a total number of 1339 subjects were included in this meta-analysis. The pooled analysis showed there was a statistically significant decrease in mean IQ in anemic children, compared to non-anemic children (-9.97, 95% CI: -17.99 to -1.96, p = 0.01, I2 = 99%). All of the studies have a low risk of bias. Conclusion: A decrease in mean IQ in children under 18 years old is associated with the presence of anemia.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".