{"id":"W2034452455","doi":"10.1016/j.apmr.2009.08.012","title":"Article 9 (see Poster 1, Part 2): Utilizing the Assessment of Motor and Process Skills Ability Measures to Predict Level of Community Dependence","year":2009,"lang":"en","type":"article","venue":"Archives of Physical Medicine and Rehabilitation","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Rasch model; Cutoff; Receiver operating characteristic; Raw score; Set (abstract data type); Sample (material); Process (computing); Measure (data warehouse); Psychology; Polytomous Rasch model; Medical diagnosis; Task (project management); Computer science; Physical medicine and rehabilitation; Raw data; Artificial intelligence; Machine learning; Statistics; Psychometrics; Item response theory; Data mining; Medicine; Mathematics; Clinical psychology; Developmental psychology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001468571,0.0005885917,0.0003239207,0.0006057113,0.0004587277,0.000685755,0.0004387284,0.001165353,0.01140629],"category_scores_gemma":[0.001449577,0.0002340751,0.0005580917,0.0002599432,0.0001416009,0.000586913,0.0005021375,0.0009918063,0.003321162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002038001,"about_ca_system_score_gemma":0.0005074571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001730497,"about_ca_topic_score_gemma":0.003724197,"domain_scores_codex":[0.9997002,0.0000887635,0.00003083264,0.00005570635,0.00007663255,0.00004791958],"domain_scores_gemma":[0.9995384,0.0001264781,0.0000519833,0.00001854728,0.0001233107,0.0001412844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.008869622,0.01753442,0.6822106,0.000268763,0.0002133666,0.0007067256,0.0004771842,0.0005487759,0.02709071,0.0001762486,0.01670919,0.2451943],"study_design_scores_gemma":[0.001113325,0.01671783,0.9627658,0.000128382,0.0002107581,0.001093633,0.0003113274,0.001486503,0.01190513,0.0004031268,0.003800148,0.00006394189],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858307,0.0003850609,0.001675789,0.001017868,0.0003539564,0.001177554,0.001283058,0.0001087973,0.008167177],"genre_scores_gemma":[0.9706824,0.0006921204,0.008071903,0.000832177,0.0003196722,0.001348314,0.001460772,0.00004474181,0.01654787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01140629,"threshold_uncertainty_score":0.03815788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03961469780015772,"score_gpt":0.3525546407251892,"score_spread":0.3129399429250315,"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."}}