Small for gestational age preterm infants and later adiposity and height: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Overweight and obesity and their consequent morbidities are important worldwide health problems. Some research suggests excess adiposity origins may begin in fetal life, but unknown is whether this applies to infants born preterm. Objective The objective of the study was to assess the association between small for gestational age (SGA) birth and later adiposity and height among those born preterm. Data sources MEDLINE, EMBASE and CINAHL until October 2022. Study selection and data extraction Studies were included if they reported anthropometric (adiposity measures and height) outcomes for participants born preterm with SGA versus non‐SGA. Screening, data extraction and risks of bias assessments were conducted in duplicate by two reviewers. Synthesis We meta‐analysed across studies using random‐effects models and explored potential heterogeneity sources. Results Thirty‐nine studies met the inclusion criteria. In later life, preterm SGA infants had a lower body mass index (−0.66 kg/m2, 95% CI −0.79, −0.53; 32 studies, I2 = 16.7, n = 30,346), waist circumference (−1.20 cm, 95% CI −2.17, −0.23; 13 studies, I2 = 19.4, n = 2061), lean mass (−2.62 kg, 95% CI −3.45, 1.80; 7 studies, I2 = 0, n = 205) and height (−3.85 cm, 95% CI −4.73, −2.96; 26 studies, I2 = 52.6, n = 4174) compared with those preterm infants born non‐SGA. There were no differences between preterm SGA and preterm non‐SGA groups in waist/hip ratio, body fat, body fat per cent, truncal fat per cent, fat mass index or lean mass index, although power was limited for some analyses. Studies were rated at high risk of bias due to potential residual confounding and low risk of bias in other domains. Conclusions Compared to their preterm non‐SGA peers, preterm infants born SGA have lower BMI, waist circumference, lean body mass and height in later life. No differences in adiposity were observed between SGA preterm infants and non‐SGA preterm infants.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".