{"doi":"10.1002/aur.2643","title":"What are we optimizing for in autism screening? Examination of algorithmic changes in the <scp>M‐CHAT</scp>","abstract":"The present study objectives were to examine the performance of the new M-CHAT-R algorithm to the original M-CHAT algorithm. The main purpose was to examine if the algorithmic changes increase identification of children later diagnosed with ASD, and to examine if there is a trade-off when changing algorithms. We included 54,463 screened cases from the Norwegian Mother and Child Cohort Study. Children were screened using the 23 items of the M-CHAT at 18 months. Further, the performance of the M-CHAT-R algorithm was compared to the M-CHAT algorithm on the 23-items. In total, 337 individuals were later diagnosed with ASD. Using M-CHAT-R algorithm decreased the number of correctly identified ASD children by 12 compared to M-CHAT, with no children with ASD screening negative on the M-CHAT criteria subsequently screening positive utilizing the M-CHAT-R algorithm. A nonparametric McNemar's test determined a statistically significant difference in identifying ASD utilizing the M-CHAT-R algorithm. The present study examined the application of 20-item MCHAT-R scoring criterion to the 23-item MCHAT. We found that this resulted in decreased sensitivity and increased specificity for identifying children with ASD, which is a trade-off that needs further investigation in terms of cost-effectiveness. However, further research is needed to optimize screening for ASD in the early developmental period to increase identification of false negatives.","journal":"Autism Research","year":2021,"id":189551,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9077,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":263479,"name":"Frederick Shic","orcid":"0000-0002-9040-1259","position":1,"is_corresponding":false},{"id":40252,"name":"Fred R. Volkmar","orcid":"0000-0002-4248-3384","position":2,"is_corresponding":false},{"id":752251,"name":"Anders Nordahl‐Hansen","orcid":"0000-0002-6411-3122","position":3,"is_corresponding":false},{"id":752881,"name":"Nina Stenberg","orcid":null,"position":4,"is_corresponding":false},{"id":752252,"name":"Tonje Torske","orcid":"0000-0002-1194-7369","position":5,"is_corresponding":false},{"id":752253,"name":"Kenneth Larsen","orcid":"0000-0002-4139-2421","position":6,"is_corresponding":false},{"id":752882,"name":"Katherine D. Riley","orcid":null,"position":7,"is_corresponding":false},{"id":376093,"name":"Denis G. Sukhodolsky","orcid":"0000-0002-5401-792X","position":8,"is_corresponding":false},{"id":310632,"name":"James F. Leckman","orcid":"0000-0002-3902-4478","position":9,"is_corresponding":false},{"id":263470,"name":"Katarzyna Chawarska","orcid":"0000-0002-1445-6346","position":10,"is_corresponding":false},{"id":752254,"name":"Roald A. Øien","orcid":"0000-0003-3698-2184","position":11,"is_corresponding":false},{"id":442749,"name":"Synnve Schjølberg","orcid":null,"position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":null,"created_at":"2026-07-18T23:49:18.604374Z","pmid":"34837355","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}