Disproportionate early fetal growth predicts postnatal thymic size in humans
Bibliographic record
Abstract
Prenatal events can affect neonatal thymus size and adult immune function. The causal insults are unknown, although fetal nutrient restriction is suspected. We used ultrasound at three time points during pregnancy (14, 19 and 30 weeks) to measure the growth of six fetal dimensions in rural Bangladeshi women participating in the Maternal and Infant Nutrition Interventions, Matlab study. Postnatal ultrasound was used to calculate thymic index (TI) at birth, 2, 6 and 12 m. Of the 3267 women recruited, 2861 participated by providing data at least at one fetal biometry and one TI time point. Patterns of fetal growth were summarized using principal components calculated from fetal dimension z-scores. Random effects regression, controlling for infant size and season of measurement were used to relate these patterns to TI. We found that smaller leg length relative to head circumference, characteristic of head-sparing growth restriction, was predictive of lower TI. This association was significant at all time points but strongest in earlier pregnancy. Each standard deviation increase in leg-head proportion was associated with an increase in TI of ∼5%. We conclude that growth patterns typical of poor fetal nutrition are associated with poor thymic development. The greater strength of this association in the first trimester is consistent with a period of vulnerability during the early ontogeny of the thymus and suggests that preventative intervention would need to be given in early pregnancy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".