{"id":"W2808504040","doi":"10.1039/c8lc00310f","title":"Dynamic CTC phenotypes in metastatic prostate cancer models visualized using magnetic ranking cytometry","year":2018,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Cancer Cells and Metastasis","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Public Health","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Connaught Fund; Ontario Research Foundation","keywords":"Prostate cancer; Phenotype; Flow cytometry; Prostate; Ranking (information retrieval); Cytometry; Cancer; Cancer research; Pathology; Medicine; Computational biology; Oncology; Biology; Internal medicine; Computer science; Immunology; Artificial intelligence; Genetics; Gene","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":[0.0002112916,0.000190509,0.0001798329,0.0004069346,0.0001701564,0.0002825185,0.000189195,0.0002691554,0.0007448065],"category_scores_gemma":[0.0002086076,0.0001246615,0.000138339,0.0001999706,0.0001538652,0.0001817391,0.0001861476,0.0005313716,0.0001802288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003093758,"about_ca_system_score_gemma":0.0001882375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001943722,"about_ca_topic_score_gemma":0.002575443,"domain_scores_codex":[0.999843,0.00003181555,0.00000634469,0.00002966582,0.00005670113,0.00003244622],"domain_scores_gemma":[0.99988,0.00002890505,0.00002840215,0.00001304921,0.00002496933,0.00002477244],"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":[0.00003421559,0.00002534663,0.0004408031,0.00001533617,0.000002702326,0.00003055796,0.00002067168,0.0003177309,0.996151,0.0002145064,0.0001284766,0.002618714],"study_design_scores_gemma":[0.00003404968,0.0005438211,0.01194093,0.000008802079,0.00002382639,0.0004599146,0.00005684521,0.02911388,0.9534221,0.0004097829,0.00396244,0.00002353077],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9168498,0.001448704,0.07611425,0.0003028798,0.000039592,0.0001383772,0.0005875394,0.0007193885,0.003799448],"genre_scores_gemma":[0.9683939,0.0007483762,0.02760541,0.000111567,0.00001446138,0.0001119233,0.0003911749,0.00004975982,0.002573411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001943722,"threshold_uncertainty_score":0.003864765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03948574811476541,"score_gpt":0.3612825125694999,"score_spread":0.3217967644547345,"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."}}