Perinatal Factors Associated with Infant Maltreatment
Bibliographic record
Abstract
Background The association between birth outcomes and child maltreatment remains controversial. The purpose of this study is to test whether infants without congenital or chronic disease who are low birth weight (LBW), preterm, or small for gestational age (SGA) are at an increased risk of being maltreated. Methods A hospital-based case-control study of infants without congenital or chronic diseases who visited the National Center for Child Health and Development, Tokyo, between April 1, 2002 and March 31, 2005 was conducted. Cases (N = 35) and controls (N = 29) were compared on mean birth weight, gestational age, and z-score of birth weight. Results SGA was significantly associated with infant maltreatment after adjusting for other risk factors (adjusted odds ratio: 4.45, 95% CI: 1.29–15.3). LBW and preterm births were not associated with infant maltreatment. Conclusion Infants born as SGA are 4.5 times more at risk of maltreatment, even if they do not have a congenital or chronic disease. This may be because SGA infants tend to have poorer neurological development which leads them to be hard-to-soothe and places them at risk for maltreatment. Abbreviations SCAN, Suspected Child Abuse and Neglect; LBW, low birth weight; ZBW, z-score of birth weight adjusted for gestational age, sex, and parity; SGA, small for gestational age; SD, standard deviation; OR, odds ratio; aOR, adjusted odds ratio; CI, confidence interval; IPV, intimate partner violence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".