Low birth weight and intelligence in adolescence and early adulthood: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Research has demonstrated an association between low birth weight (LBW; <2500 g) and adverse intelligence quotient (IQ) outcomes in childhood and early adolescence. We systematically evaluated whether this association persists into late adolescence and early adulthood and also assessed the influence of age of IQ assessment on effect size. METHODS: During Stage 1 (meta-analysis of data on adolescents/adults), we searched for relevant articles in PsychINFO, PubMed, Ovid, CINAHL, ProQuest and ERIC until February 2011 (no lower limit). Studies which assessed full-scale IQ among LBW individuals (<2500 g), aged 13 years and older, with a normal birth weight (NBW; ≥2500 g) comparison group were eligible. A random-effects meta-analysis provided a pooled estimate of the difference in IQ scores between LBW and NBW individuals. Publication bias was assessed using Rosenthal's classic fail-safe N and Duval and Tweedie's Trim and Fill. During Stage 2, we added data from the Kerr-Wilson et al. meta-analysis (which included data from children; in Meta-analysis of the association between preterm delivery and intelligence. Journal Public Health 2011;33:1-8) to our sample from Stage 1 and conducted a meta-regression to evaluate the effect of age of IQ assessment. RESULTS: Using a total of 15 studies in Stage 1, it was demonstrated that NBW individuals scored an average of 7.63 IQ points higher than LBW individuals, CI = 5.95-9.31. After adjusting for publication bias, NBW samples demonstrated an IQ of 4.98 points higher than LBW samples, CI = 3.20-6.77. Furthermore, age at IQ assessment was a significant moderator of the association between birth weight and IQ, in that the effect size decreased from childhood into young adulthood. CONCLUSIONS: Cognitive impairments associated with LBW persist into adolescence and early adulthood; however, the influence of LBW on IQ decreases from childhood to young adulthood. These conclusions must be interpreted with caution due to unmeasured variables and possible influence from publication bias.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.049 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".