{"id":"W2593207864","doi":"10.5430/air.v6n2p1","title":"Predicting rehabilitation treatment helpfulness to stroke patients: A supervised learning approach","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Rehabilitation; Stroke (engine); Helpfulness; Medicine; Physical therapy; Physical medicine and rehabilitation; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001673577,0.0001737028,0.0003212372,0.0004883405,0.001257511,0.0002682817,0.0002712765,0.0001284602,0.0001497873],"category_scores_gemma":[0.0104261,0.0001425761,0.0001846722,0.0003031458,0.0003554338,0.0002532195,0.0001365094,0.0004452913,0.0003939514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004663899,"about_ca_system_score_gemma":0.0002006423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007224238,"about_ca_topic_score_gemma":0.00008409106,"domain_scores_codex":[0.9970505,0.0003545689,0.0004731617,0.0005812894,0.0009295083,0.000610976],"domain_scores_gemma":[0.9967065,0.001067712,0.00008428192,0.0007317698,0.001047868,0.0003618063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008237213,0.001339657,0.4994856,0.0001289865,0.00006094735,0.00000533428,0.006429966,0.0005847804,0.004037634,0.001718491,0.00007594863,0.4853089],"study_design_scores_gemma":[0.00166956,0.03224945,0.6720883,0.0009907529,0.0001382224,0.00001509054,0.08383741,0.09508029,0.09714388,0.004479413,0.01122133,0.001086293],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861443,0.00002999144,0.003037771,0.002345434,0.0002793217,0.001638816,0.00001020802,0.00007325687,0.006440876],"genre_scores_gemma":[0.9940799,0.00002452843,0.00345815,0.00002649807,0.0003256304,0.0003451334,0.00002683939,0.00003082082,0.00168248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4842227,"threshold_uncertainty_score":0.9979095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1531883255040664,"score_gpt":0.4278118813084872,"score_spread":0.2746235558044208,"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."}}