{"id":"W2887195545","doi":"10.1088/1741-2552/aad872","title":"A speed-adaptive intraspinal microstimulation controller to restore weight-bearing stepping in a spinal cord hemisection model","year":2018,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Innovates; Alberta Innovates - Health Solutions","keywords":"Weight-bearing; Microstimulation; Gait; Control theory (sociology); Computer science; Lumbosacral joint; Controller (irrigation); Treadmill; Bearing (navigation); Functional electrical stimulation; Physical medicine and rehabilitation; Artificial intelligence; Medicine; Control (management); Surgery; Physical therapy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001489149,0.0003284912,0.0002029129,0.0001825173,0.0001902638,0.0002538854,0.0003781603,0.0002604277,0.0009052563],"category_scores_gemma":[0.0002388166,0.0001165983,0.0002201957,0.00007185033,0.0002085545,0.0001067904,0.0001807354,0.0002413412,0.00009431617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003776977,"about_ca_system_score_gemma":0.000596297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01163855,"about_ca_topic_score_gemma":0.01358739,"domain_scores_codex":[0.9999595,0.000004758221,0.000002767179,0.00001289271,0.00001158496,0.000008600098],"domain_scores_gemma":[0.9998912,0.00002521744,0.0000292806,0.000009470195,0.00003406773,0.00001074982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003328935,0.0002123852,0.003414129,0.0002573437,0.0000851569,0.0002116314,0.0001368755,0.8125141,0.1512935,0.001410178,0.0004452976,0.02968652],"study_design_scores_gemma":[0.00002293642,0.0002733167,0.002079321,0.00000646225,0.00002713182,0.00001938804,0.00001429808,0.987751,0.009263769,0.0001431711,0.000393279,0.000005896462],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.848839,0.0001316085,0.1445564,0.0001328786,0.00002634716,0.0001833385,0.0001574651,0.0004465432,0.005526327],"genre_scores_gemma":[0.9935625,0.00002545957,0.005256518,0.000007411121,0.0000010443,0.00007176467,0.00003698803,0.000004162333,0.001034142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01163855,"threshold_uncertainty_score":0.02314162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06070731185041772,"score_gpt":0.3632477074178689,"score_spread":0.3025403955674512,"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."}}