{"id":"W4412651079","doi":"10.1016/j.mtbio.2025.102111","title":"A dermis-on-a-chip model for compound screening","year":2025,"lang":"en","type":"article","venue":"Materials Today Bio","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University of Toronto; University Health Network; Canada Research Chairs","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Chip; Dermis; Computer science; Medicine; Pathology; Telecommunications","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":[0.0004535663,0.000844086,0.0006055962,0.0003908879,0.0002563775,0.0004981271,0.0008834877,0.001062508,0.003880735],"category_scores_gemma":[0.000285451,0.0004409115,0.0006862943,0.0002372622,0.0002303637,0.0003845512,0.0004228576,0.0008034115,0.001474067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003624175,"about_ca_system_score_gemma":0.000461394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001040502,"about_ca_topic_score_gemma":0.002122495,"domain_scores_codex":[0.9992668,0.000116263,0.00004321729,0.0002160407,0.0002722112,0.00008549447],"domain_scores_gemma":[0.999669,0.00009138173,0.00004856049,0.00006764904,0.00007259461,0.00005090852],"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":[0.000105218,0.0001479867,0.0001753984,0.0001718089,0.00003169423,0.0001115271,0.000013226,0.0009274206,0.9918623,0.0002689525,0.001203887,0.004980604],"study_design_scores_gemma":[0.00005779762,0.001418546,0.002087381,0.00002078858,0.00008332634,0.0003780272,0.00003522749,0.02090006,0.9596148,0.000229938,0.01512349,0.00005069339],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5154748,0.008674121,0.4229921,0.001781492,0.002212098,0.003428949,0.01291793,0.006738611,0.02577986],"genre_scores_gemma":[0.7030569,0.003482412,0.2666245,0.001558664,0.0001276946,0.003706612,0.004627823,0.000228234,0.0165871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003880735,"threshold_uncertainty_score":0.01298237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03773374683650549,"score_gpt":0.3088644523219961,"score_spread":0.2711307054854906,"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."}}