{"id":"W4206106878","doi":"10.1017/cjn.2021.458","title":"P.182 Self-Assembling Peptide Biomaterial to Optimize Human Stem Cell-Based Regeneration of the Injured Spinal Cord","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Tissue Engineering and Regenerative Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Public Health; Systems, Applications & Products in Data Processing (Canada)","funders":"","keywords":"Neural stem cell; Spinal cord injury; Regeneration (biology); Medicine; Stem cell; Neurosphere; Transplantation; Neurite; Regenerative medicine; Cell biology; Pathology; Extracellular matrix; In vivo; Spinal cord; In vitro; Biology; Adult stem cell; Surgery; Biochemistry; Endothelial stem cell","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001277982,0.0002949796,0.0001156422,0.0001843237,0.00008832936,0.0001810169,0.0001202408,0.0002529318,0.002222596],"category_scores_gemma":[0.0001198054,0.00009990202,0.0001418386,0.0001060367,0.0001018484,0.0001101846,0.0001451964,0.0001980905,0.0008370409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001401296,"about_ca_system_score_gemma":0.0001180231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002247605,"about_ca_topic_score_gemma":0.0003613535,"domain_scores_codex":[0.9999275,0.000009075535,0.000006151564,0.00001783426,0.00002998217,0.000009353467],"domain_scores_gemma":[0.9999654,0.000005545286,0.0000105712,0.000002337891,0.00000796831,0.000008145332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003265702,0.00002030495,0.00006408255,0.00003945806,0.000002061569,0.00002654161,0.000004965755,0.0001803785,0.9973428,0.00003319905,0.00006805749,0.002185537],"study_design_scores_gemma":[0.00001626769,0.0003983074,0.001025453,0.00000688678,0.00001105017,0.0001916026,0.00001186317,0.001421056,0.9931867,0.00002635844,0.003700477,0.000004094319],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779938,0.002575207,0.01180479,0.0001188493,0.00009046309,0.0001735496,0.0005384599,0.0001917973,0.006512986],"genre_scores_gemma":[0.9744282,0.001057598,0.01809851,0.0001201476,0.00001668809,0.0001067148,0.0004576891,0.00006208946,0.005652326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002222596,"threshold_uncertainty_score":0.007435322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03910054976221055,"score_gpt":0.2918415355989635,"score_spread":0.2527409858367529,"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."}}