{"id":"W2883240905","doi":"10.30699/mmlj17.2.1.1","title":"Bio-printing Damaged Tissues: A Novel Approach in Regenerative Medicine","year":2018,"lang":"en","type":"article","venue":"Modern Medical Laboratory Journal","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Regenerative medicine; Tissue engineering; 3D bioprinting; Stem cell; Regeneration (biology); Biology; Cell biology; Computer science; Biomedical engineering; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006980477,0.0006996495,0.0008178081,0.001368591,0.0004796622,0.001836607,0.0009081137,0.001879705,0.002633455],"category_scores_gemma":[0.000514494,0.0004120134,0.0006758706,0.001099823,0.001578677,0.001551357,0.001262906,0.001728002,0.001947233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004467425,"about_ca_system_score_gemma":0.0004540021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001599293,"about_ca_topic_score_gemma":0.0002490356,"domain_scores_codex":[0.999238,0.0001104129,0.00004749243,0.0001675951,0.0003849252,0.00005154478],"domain_scores_gemma":[0.9996191,0.000157747,0.00006950184,0.00006590319,0.00005526657,0.00003244672],"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":[0.0001199676,0.0001201532,0.0004050595,0.00411203,0.00005940442,0.00105953,0.0004872947,0.001213325,0.6381522,0.0359824,0.005162301,0.3131263],"study_design_scores_gemma":[0.000025655,0.0005999188,0.001705368,0.0006950359,0.0001466361,0.009330921,0.0002545102,0.003288405,0.5960328,0.01494602,0.372848,0.0001267177],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.0456653,0.5957148,0.2995743,0.003886439,0.003177354,0.0002438018,0.0004489019,0.001414482,0.0498747],"genre_scores_gemma":[0.3490138,0.433287,0.1770372,0.003553888,0.001968458,0.0004211362,0.0004617925,0.0003549476,0.03390178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002633455,"threshold_uncertainty_score":0.008809805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02703031966658108,"score_gpt":0.3031742062774856,"score_spread":0.2761438866109046,"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."}}