Predictors (0–10 months) of psychopathology at age 1½ years – a general population study in The Copenhagen Child Cohort CCC 2000*
Bibliographic record
Abstract
BACKGROUND: Epidemiological studies of mental health problems in the first years of life are few. This study aims to investigate infancy predictors of psychopathology in the second year of life. METHODS: A random general population sample of 210 children from the Copenhagen Child Birth Cohort CCC 2000 was investigated by data from National Danish registers and data collected prospectively from birth in a general child health surveillance programme. Mental health outcome at 1(1/2) years was assessed by clinical and standardised measures including the Child Behavior Check List 1(1/2)-5 (CBCL 1(1/2)-5), Infant Toddler Symptom Check List (ITSCL), Checklist for Autism in Toddlers (CHAT), Bayley Scales of Infant Development (BSID II), Mannheim Eltern Interview (MEI), Parent Child Early Relational Assessment (PC ERA) and Parent Infant Relationship Global Assessment Scale (PIR-GAS), and disordered children were diagnosed according to the International Classification of Diseases (ICD-10) and Diagnostic Classification Zero to Three (DC: 0-3). RESULTS: Deviant language development in the first 10 months of life predicted the child having any disorder at 1(1/2) years, OR 3.3 (1.4-8.0). Neuro-developmental disorders were predicted by deviant neuro-cognitive functioning, OR 6.8 (2.2-21.4), deviant language development, OR 5.9 (1.9-18.7) and impaired social interaction and communication, OR 3.8 (1.3-11.4). Unwanted pregnancy and parents' negative expectations of the child recorded in the first months of the child's life were significant predictors of relationship disturbances at 1(1/2) years. CONCLUSIONS: Predictors of neuro-developmental disorders and parent-child relationship disturbances can be identified in the first 10 months of life in children from the general population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".