Early developmental profiles of sensory features and links to school-age adaptive and maladaptive outcomes: A birth cohort investigation
Bibliographic record
Abstract
Abstract Sensory-based subtypes among autistic children have been well documented, but little is known about longitudinal sensory subtypes beyond autistic populations. This prospective study aimed to identify subtypes based on trajectories of parent-reported sensory features measured at 6–19 months, 3–4, and 6–7 years of age among a community-based birth cohort ( N = 1,517), and to examine their associations with school-age clinical and adaptive/maladaptive outcomes on a subset sample ( N = 389). Latent class growth analysis revealed five trajectory subtypes varying in intensity and change rates across three sensory domains. In contrast to an Adaptive-All Improving subtype (35%) with very low sensory features and overall better school-age outcomes, an Elevated-All Worsening subtype (3%), comprised of more boys and children of parents with less education, was associated with most elevated autistic traits and poorest adaptive/maladaptive outcomes. Three other subtypes (62% in total) were generally characterized by stable or improving patterns of sensory features at mild to moderate levels, and challenges in certain outcome domains. Our findings indicate that characterizing children based on early sensory trajectories may contribute to earlier detection of subgroups of children with sensory challenges who are more likely to experience developmental challenges by school age, followed by early targeted interventions for improved long-term outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".