{"id":"W4308744595","doi":"10.1016/j.bioactmat.2022.10.029","title":"Cell–scaffold interactions in tissue engineering for oral and craniofacial reconstruction","year":2022,"lang":"en","type":"article","venue":"Bioactive Materials","topic":"Bone Tissue Engineering Materials","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Scaffold; Tissue engineering; Regenerative medicine; Regeneration (biology); Craniofacial; Extracellular matrix; Cell adhesion; Biomaterial; Process (computing); Materials science; Nanotechnology; Biomedical engineering; Cell; Chemistry; Adhesion; Cell biology; Computer science; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000231867,0.0001936056,0.0002936262,0.000216664,0.00007876027,0.00006690749,0.00008417681,0.00004028705,0.0006568913],"category_scores_gemma":[0.00002065561,0.000241263,0.00002318218,0.0001236643,0.00001570494,0.000191171,0.00006133367,0.00008756023,0.00001129677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002014262,"about_ca_system_score_gemma":0.000008857392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007426538,"about_ca_topic_score_gemma":0.000006514736,"domain_scores_codex":[0.9991129,0.00003546927,0.0002973129,0.0002104549,0.00008084293,0.0002630186],"domain_scores_gemma":[0.9997275,0.00004990525,0.00004134731,0.0001197528,0.00001472993,0.00004678383],"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":[0.00003578359,0.00001579888,0.00003039491,0.0001136533,0.00001749162,0.000002939626,0.0002219644,0.009429689,0.988437,0.0001622468,0.0002223839,0.001310646],"study_design_scores_gemma":[0.0007980023,0.00007766204,0.002508823,0.00002446044,0.00002234909,0.00006582524,0.0002040882,0.002597625,0.9671192,0.0001205324,0.02603054,0.0004309459],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922397,0.00007348951,0.002057929,0.00002597849,0.004233098,0.0005328304,0.0003942637,0.0003034654,0.0001392157],"genre_scores_gemma":[0.995917,0.00001146065,0.003188407,0.000005292565,0.0001463154,0.0005176957,0.00003384144,0.00006784809,0.0001122004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02580816,"threshold_uncertainty_score":0.9838421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01308424859606436,"score_gpt":0.2268051029769385,"score_spread":0.2137208543808741,"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."}}