{"id":"W2395552044","doi":"10.1002/jbm.a.35715","title":"Winner of the Young Investigator Award of the Society for Biomaterials at the 10th World Biomaterials Congress, May 17–22, 2016, Montreal QC, Canada: Microribbon‐based hydrogels accelerate stem cell‐based bone regeneration in a mouse critical‐size cranial defect model","year":2016,"lang":"en","type":"article","venue":"Journal of Biomedical Materials Research Part A","topic":"Bone Tissue Engineering Materials","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Dental and Craniofacial Research; National Institute of General Medical Sciences","keywords":"Self-healing hydrogels; Stem cell; Paracrine signalling; Stromal cell; Materials science; Mesenchymal stem cell; Biomedical engineering; Cell biology; Regenerative medicine; Transplantation; Tissue engineering; Bone healing; Medicine; Cancer research; Biology; Surgery; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003843623,0.00174973,0.001023235,0.001613614,0.00161822,0.002961022,0.001581611,0.002868697,0.04886305],"category_scores_gemma":[0.001148731,0.000350185,0.0009382997,0.0006090028,0.0006560457,0.0007549659,0.003062705,0.001618694,0.01214012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004953376,"about_ca_system_score_gemma":0.02060681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04589443,"about_ca_topic_score_gemma":0.1391498,"domain_scores_codex":[0.9985147,0.00007098012,0.0000443384,0.0001437539,0.0007497942,0.0004765423],"domain_scores_gemma":[0.9962812,0.00004034047,0.00006854854,0.0000288549,0.001322628,0.002258493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000358871,0.0002595811,0.001183962,0.0002805553,0.0000509749,0.0002217118,0.00008107008,0.000239201,0.008738894,0.001228941,0.8922364,0.09511992],"study_design_scores_gemma":[0.000193981,0.0004261427,0.009397201,0.0001984543,0.00006059756,0.0001619944,0.0002192746,0.001025941,0.004781659,0.0005463576,0.9829301,0.00005841589],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.04734706,0.09197026,0.01146529,0.1692309,0.5105308,0.003620022,0.008948142,0.00141776,0.1554698],"genre_scores_gemma":[0.04109073,0.02635601,0.003618645,0.005219452,0.03025018,0.0005162778,0.004829627,0.0003381089,0.887781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04886305,"threshold_uncertainty_score":0.1634632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03101360711616936,"score_gpt":0.2744640079276173,"score_spread":0.243450400811448,"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."}}