Prematurity and Body Fat at 6, 18, and 30 Years of Age: Pelotas (Brazil) 2004, 1993, and 1982 Birth Cohorts
Bibliographic record
Abstract
Abstract Background: Our aim was to investigate the association between preterm birth and body fat at 6, 18, and 30 years of age using data from three population-based birth cohort studies. Methods: Information on gestational age (GA) gathered in the hospital of birth in the first 24-hours after the delivery was obtained for all live births occurring in the city of Pelotas, Brazil, in the years 2004, 1993 and 1982. GA was defined by the date of last menstrual period and was later categorized in ≤33, 34-36 and ≥37 weeks. Body fat was assessed by air-displacement plethysmography. Outcomes included fat mass (FM, kg), percent fat mass (%FM), fat mass index (FMI, kg/m2), and body mass index (BMI, kg/m2 at 18 years in the 1993 cohort and at 30 years in the 1982 cohort; and BMI Z-score, at 6 years in the 2004 cohort). Crude and adjusted linear regression provided beta coefficients with 95% confidence intervals (95%CI).Results: A total of 3036, 3027, and 2417 participants, respectively, from the 2004, 1993, and 1982 cohorts were analyzed. At 6 years, boys born at 34-36 weeks GA presented lower adjusted mean %FM (β: -2.91%; -4.45--1.36), FMI (β: -0.70 kg/m2 ; -1.13--0.28) and BMI Z-score (β: -0.48 kg/m2; -0.79--0.16), when compared to boys born at term (≥37). At 30 years, FM (15.6kg; 0.40-30.90), %FM (13.65%; 1.38-25.92) and FMI (5.3kg/m2; 0.30-10.37) were higher among males born at ≤33 weeks, with no statistical difference as compared to those born at term. No association was found between GA and body fat at the 1993 cohort (18 years) for both sexes. Conclusions: Given the large number of preterm infants born each year, prevention of prematurity is essential as there are possible links between body composition and diseases later in life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".