{"id":"W4379984688","doi":"10.1158/1538-7445.am2023-5526","title":"Abstract 5526: State-of-the-art biobanking of gastroesophageal adenocarcinoma samples: Integrating non-viable biospecimens with 3D and 2D cell models and their characterization, validation, and utilization of cell models for precision oncology","year":2023,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Cells and Metastasis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Biobank; Context (archaeology); Precision medicine; Translational research; Computational biology; Biorepository; Personalized medicine; Computer science; Bioinformatics; Medicine; Biology; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003566355,0.0006184911,0.0006159216,0.001341931,0.0006655225,0.00181621,0.0009529233,0.0008610159,0.003119502],"category_scores_gemma":[0.001604463,0.0004173589,0.0005585616,0.001138406,0.0006086621,0.0008246291,0.001636706,0.001060449,0.003051211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005466301,"about_ca_system_score_gemma":0.001312726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00133364,"about_ca_topic_score_gemma":0.002102151,"domain_scores_codex":[0.9985475,0.0002986105,0.0001597437,0.0002823154,0.0006222521,0.00008955702],"domain_scores_gemma":[0.9990259,0.0001544501,0.00009231023,0.0002484185,0.0003325047,0.0001464195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000498023,0.0002013572,0.009518961,0.0005370597,0.00007479774,0.0005679981,0.0003964744,0.001462898,0.915834,0.001852245,0.008598783,0.06045731],"study_design_scores_gemma":[0.00006795135,0.0007592727,0.03145979,0.0002196949,0.0001914202,0.002574568,0.0004805698,0.007673774,0.7758876,0.002238288,0.1783341,0.0001129094],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6173676,0.01858569,0.3008865,0.003118928,0.001030389,0.002056705,0.03430227,0.003898905,0.01875301],"genre_scores_gemma":[0.4961684,0.01068985,0.4035604,0.001369451,0.0002741875,0.002029492,0.07486093,0.0009471932,0.01010009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003566355,"threshold_uncertainty_score":0.01886094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1190190879669486,"score_gpt":0.3667264815173453,"score_spread":0.2477073935503967,"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."}}