Early Start Denver Model effectiveness in young autistic children: a large multicentric randomised controlled trial in two European countries
Bibliographic record
Abstract
BACKGROUND: Evidence regarding early interventions based on the Naturalistic Developmental Behavioral Interventions framework, such as the Early Start Denver Model (ESDM), suggests efficacy for autistic children. However, the effectiveness of ESDM across diverse cultural contexts remains under-researched. OBJECTIVE: To assess the effectiveness of ESDM compared with treatment as usual (TAU) on overall development in young children with autism spectrum disorder (ASD). METHOD: This parallel, randomised controlled trial, using a modified Zelen design, was conducted in five Child and Adolescent Mental Health Services in France and Belgium. A total of 180 children aged 19-36 months, who met autism criteria and were referred by community professionals, were randomly assigned to either receive 12-hour weekly ESDM+TAU or TAU alone. The primary outcome was the change in developmental quotient (DQ) on the Mullen Scale of Early Learning, assessed blindly from baseline to 24 months post randomisation. FINDINGS: From September 2015 to March 2019, 180 children were randomly assigned to either ESDM+TAU (n=61, girls=21.7%) or TAU alone (n=119, girls=15.4%). Three children dropped out immediately after randomisation. Compared with TAU alone, children in the ESDM+TAU group did not significantly improve global DQ (endpoint mean difference 3.82 (95% CI -1.25 to 8.89), p=0.14). CONCLUSIONS AND CLINICAL IMPLICATIONS: Our findings suggest that ESDM+TAU cannot be universally recommended for young children with ASD. Further research is required to evaluate the long-term effectiveness of ESDM and identify subgroups that may benefit more, thereby guiding optimal implementation strategies. TRIAL REGISTRATION NUMBER: NCT02608333.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".