A comparative efficacy of holistic multidimensional treatment model (HMTM) and applied behavioral analysis (ABA) in the treatment of children with autism spectrum disorder (ASD)
Bibliographic record
Abstract
Introduction Several approaches have been presented for treatment of children with ASD. The aim of the present study was to compare the efficacy of Holistic Multidimensional Treatment Model (HMTM) with Applied Behavioral Analysis (ABA) in the improvement of clinical symptoms of children with ASD. Method The present study was an experimental pre and post test research. The statistical population included the 3 to 8 year old children with ASD who were referred to the child psychiatry clinics in two academic Hospitals- Mashhad,Iran. The sample included 20 children who were selected with convenience sampling and randomly divided to 2 groups of ABA (8 boys and 2 girls) and HMTM (7 boys and 3 girls).The diagnosis was made by a child psychiatrist based on DSM-IV-TR criteria and using Autism Diagnostic Interview-Revised (ADIR) and Autism Diagnostic Observation Schedule (ADOS).Then the children were referred to Noore Hedayat center for the treatment. Childhood Autism Rating Scale (CARS), Bender Gestalt Test, Draw-A-Man Test Good enough, Raven's Colored Progressive Matrices Test for children, Vineland Social Maturity Scale (VSMS) and performance charts of children based on their videotaped behaviors. Data analysis was done using ANCOVA test. Results Findings showed that in spite of more efficacy of HMTM compared to ABA, the differences of standard tests except Bender Gestalt Test and performance charts did not reach to the significant level(p > 0.05). Conclusion In conclusion, HMTM at least had the equal efficacy to ABA in the treatment of children with Autism Spectrum Disorder. Further researches are needed to compare the efficacy of these 2 methods.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".