{"id":"W4210485387","doi":"10.1098/rsif.2021.0734","title":"Application of an organotypic ocular perfusion model to assess intravitreal drug distribution in human and animal eyes","year":2022,"lang":"en","type":"article","venue":"Journal of The Royal Society Interface","topic":"Retinal Diseases and Treatments","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Public Health Ontario; University Health Network","funders":"National Eye Institute; Canadian Institutes of Health Research; University Health Network Foundation; Genentech","keywords":"Human eye; Posterior segment of eyeball; Retinal; Intraocular pressure; Perfusion; In vivo; Drug delivery; Ophthalmology; Medicine; Neuroscience; Biomedical engineering; Biology; Computer science; Nanotechnology; Materials science; Artificial intelligence; Biotechnology; Internal medicine","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":[0.0006626497,0.0004564105,0.0002147362,0.0003066115,0.0002908774,0.0004757718,0.0001975753,0.0004846005,0.001488857],"category_scores_gemma":[0.0002485986,0.0002072668,0.0003208649,0.000141626,0.0004212144,0.0003660706,0.0002848632,0.0007789039,0.0003482972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003609866,"about_ca_system_score_gemma":0.0004687369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002510771,"about_ca_topic_score_gemma":0.002784164,"domain_scores_codex":[0.999785,0.00004033144,0.0000126292,0.00006393437,0.00004581768,0.00005230947],"domain_scores_gemma":[0.9997299,0.00007340663,0.00007065947,0.00004326191,0.00004656665,0.00003609135],"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":[0.0001238911,0.0000406632,0.0003054397,0.00004702523,0.000008595401,0.0001100631,0.00006578495,0.0002419653,0.996767,0.0001725879,0.0000869165,0.002029968],"study_design_scores_gemma":[0.00001894217,0.001606605,0.007832442,0.0000249593,0.0000708286,0.0005125477,0.000171767,0.004049188,0.9814262,0.0001311567,0.004127686,0.00002772487],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8923793,0.003310971,0.09623021,0.0003041748,0.0002687958,0.0003641592,0.001049702,0.0005109382,0.005581698],"genre_scores_gemma":[0.971056,0.001580957,0.02288915,0.0001617197,0.00002455228,0.0002914931,0.0004604872,0.00006918703,0.003466553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002510771,"threshold_uncertainty_score":0.004992366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0116352990368781,"score_gpt":0.3015120799257765,"score_spread":0.2898767808888984,"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."}}