{"id":"W3044614940","doi":"10.1186/s11689-020-09321-6","title":"Towards greater transparency in neurodevelopmental disorders research: use of a proposed workflow and propensity scores to facilitate selection of matched groups","year":2020,"lang":"en","type":"article","venue":"Journal of Neurodevelopmental Disorders","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; International Laboratory for Brain, Music and Sound Research; McGill University; Centre for Research on Brain Language and Music","funders":"Canadian Institutes of Health Research; Sinneave Family Foundation; Autism Speaks","keywords":"Propensity score matching; Selection bias; Neurotypical; Workflow; Covariate; Matching (statistics); Selection (genetic algorithm); Population; Documentation; Transparency (behavior); Autism spectrum disorder; Psychology; Computer science; Autism; Applied psychology; Clinical psychology; Medicine; Machine learning; Developmental psychology; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006899735,0.0003654313,0.0007423668,0.000930813,0.0001660131,0.00008831103,0.0006275407,0.00009427995,0.00007285956],"category_scores_gemma":[0.00105708,0.0003289898,0.0001319325,0.002457383,0.0005216899,0.0008895095,0.0003736423,0.0008805612,0.000007678742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001088533,"about_ca_system_score_gemma":0.0004908533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002301252,"about_ca_topic_score_gemma":0.0005345289,"domain_scores_codex":[0.9948489,0.0007521454,0.001330759,0.0007270169,0.001600276,0.0007408855],"domain_scores_gemma":[0.9987608,0.0002167371,0.0003639436,0.0001797848,0.0001007959,0.0003779973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004687294,0.001013594,0.527454,0.0005760448,0.00005322452,0.0001032114,0.0304675,0.0006740688,0.4097657,0.0002138724,0.0002328329,0.02475869],"study_design_scores_gemma":[0.002226383,0.001802576,0.9324955,0.0001909964,0.00001174912,0.00004914193,0.0005865674,0.0003720399,0.06072329,0.0009280307,0.0002364363,0.0003772597],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910368,0.00002890548,0.0004013549,0.006929196,0.0001058868,0.001297331,0.00002688282,0.0000239144,0.0001497454],"genre_scores_gemma":[0.9971775,0.0005158706,0.001926653,0.0002621024,0.000008751475,0.00001766895,0.000002100856,0.00005452901,0.00003479747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4050415,"threshold_uncertainty_score":0.9999162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1970697452095999,"score_gpt":0.3144251633110896,"score_spread":0.1173554181014897,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}