Impact of extremely low‐birth‐weight status on risk and resilience for depression and anxiety in adulthood
Bibliographic record
Abstract
BACKGROUND: Preterm birth is associated with an increased risk of depression and anxiety, but it is not known if this is due to greater exposure to risk, or if perinatal adversity amplifies the impact of traditional risk factors. This study sought to determine if exposure to perinatal adversity modifies associations between traditional risk and resilience factors and depression and anxiety in adulthood. METHODS: A sample of 142 extremely low-birth-weight (ELBW < 1,000 g) survivors and 133 sociodemographically matched normal birth weight (NBW) control participants was followed longitudinally to 22-26 years of age. Separate postnatal risk and resilience scales were created using eight risk and seven resilience factors, respectively. Depression and anxiety were assessed using the internalizing scale of the Young Adult Self-Report (YASR). This scale was also dichotomized at the 90th percentile to define clinically significant psychopathology. RESULTS: While the average number of risk exposures did not differ between groups, ELBW survivors were more susceptible to risk than NBW control participants. For the ELBW group, each additional risk factor resulted in a 2-point increase in internalizing scores, and two and a half times the odds of clinically significant internalizing symptoms (OR = 2.47, 95% CI = 1.63, 3.76). The protective effect of resiliency factors was also blunted among ELBW survivors. CONCLUSIONS: Extremely low-birth-weight survivors may be more sensitive to traditional risk factors for psychopathology and less protected by resiliency factors. Intervention strategies aimed at preventing or reducing exposure to traditional childhood risk factors for psychopathology may reduce the burden of mental illness in adult survivors of prematurity.
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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 | 0.000 | 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 | 0.000 |
| 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".