Mental health of extremely low birth weight survivors: A systematic review and meta-analysis.
Bibliographic record
Abstract
Although individuals born at extremely low birth weight (ELBW; < 1,000 g) are the most vulnerable of all preterm survivors, their risk for mental health problems across the life span has not been systematically reviewed. The primary objective of this systematic review and meta-analysis was to ascertain whether the risk for mental health problems is greater for ELBW survivors than their normal birth weight (NBW) peers in childhood, adolescence, and adulthood. Forty-one studies assessing 2,712 ELBW children, adolescents, and adults and 11,127 NBW controls were reviewed. Group differences in mental health outcomes were assessed using random effects meta-analyses. The impacts of birthplace, birth era, and neurosensory impairment on mental health outcomes were assessed in subgroup analyses. Children born at ELBW were reported by parents and teachers to be at significantly greater risk than NBW controls for inattention and hyperactivity, internalizing, and externalizing symptoms. ELBW children were also at greater risk for conduct and oppositional disorders, autistic symptoms, and social difficulties. Risks for parent-reported inattention and hyperactivity, internalizing, and social problems were greater in adolescents born at ELBW. In contrast, ELBW teens self-reported lower inattention, hyperactivity, and oppositional behavior levels than their NBW peers. Depression, anxiety, and social difficulties were elevated in ELBW survivors in adulthood. Group differences were robust to region of birth, era of birth, and the presence of neurosensory impairments. The complex needs faced by children born at ELBW continue throughout development, with long-term consequences for psychological and social well-being. (PsycINFO Database Record
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".