Extremely low birth weight babies grown up: Gene–environment interaction predicts internalizing problems in the third and fourth decades of life
Bibliographic record
Abstract
Extremely low birth weight (ELBW; <1000 g) infants have been exposed to stressful intrauterine and early postnatal environments. Even greater early adversity has been experienced by ELBW survivors who were also born small for gestational age (SGA; <10th percentile for GA) compared to those born appropriate for GA (AGA). ELBW survivors, particularly those born SGA, face increased risk for internalizing problems compared to normal BW (NBW; ≥2500 g) controls. Internalizing problems are related to allelic variations in the promoter region of the serotonin transporter linked polymorphic region gene (5-HTTLPR). We followed the oldest longitudinal cohort of ELBW survivors to adulthood. Participants provided buccal cells and reported on internalizing problems, using the Young Adult Self-Report when they were in their mid-20s (ELBW/SGA, N = 28; ELBW/AGA, N = 60; NBW, N = 81) and mid-30s (ELBW/SGA, N = 27; ELBW/AGA, N = 58; NBW, N = 76). The findings indicate that ELBW/SGAs carrying the 5-HTTLPR short allele reported increased internalizing problems, particularly depression, during the third and fourth decades of life. This is the first known report on gene-environment interactions predicting psychopathology among ELBW survivors. Our findings elucidate putative neurobiological pathways that underlie risk for psychopathology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".