A longitudinal comparison of emotional, behavioral and attention problems in autistic and typically developing children
Bibliographic record
Abstract
BACKGROUND: Mental health problems are elevated in autistic individuals but there is limited evidence on the developmental course of problems across childhood. We compare the level and growth of anxious-depressed, behavioral and attention problems in an autistic and typically developing (TD) cohort. METHODS: = 884, 49% boys). Percentile plots were generated to quantify the differences between autistic and TD children. RESULTS: Autistic children showed elevated levels of mental health problems, but this was substantially reduced by accounting for IQ and sex differences between the autistic and TD samples. There was small differences in growth patterns; anxious-depressed problems were particularly elevated at preschool and attention problems at late childhood. Higher family income predicted lower base-level on all three dimensions, but steeper increase of anxious-depressed problems. Higher IQ predicted lower level of attention problems and faster decline over childhood. Female sex predicted higher level of anxious-depressed and faster decline in behavioral problems. Social-affect autism symptom severity predicted elevated level of attention problems. Autistic girls' problems were particularly elevated relative to their same-sex non-autistic peers. CONCLUSIONS: Autistic children, and especially girls, show elevated mental health problems compared to TD children and there are some differences in predictors. Assessment of mental health should be integrated into clinical practice for autistic children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".