{"id":"W2749521159","doi":"10.1182/blood.v128.22.5206.5206","title":"Ex Vivo High-Throughput Flow Cytometry Screening Identifies Subsets of Responders to Differentiation Agents in Individual AML Patient Samples","year":2016,"lang":"en","type":"article","venue":"Blood","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"","keywords":"Ex vivo; Acute promyelocytic leukemia; Flow cytometry; Myeloid; Population; Haematopoiesis; Progenitor cell; Immunophenotyping; Biology; Leukemia; Myeloid leukemia; In vivo; CD34; Cancer research; Immunology; Stem cell; Retinoic acid; Medicine; Cell culture; Cell biology","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":[0.0003275862,0.0002906964,0.0003586082,0.0005810307,0.0002356491,0.0004586779,0.0002076606,0.0003878969,0.001883857],"category_scores_gemma":[0.000396602,0.0001026024,0.0001575496,0.000277146,0.0001521572,0.0001432021,0.000198607,0.0003678826,0.0004965882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001500483,"about_ca_system_score_gemma":0.0001396853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001834024,"about_ca_topic_score_gemma":0.0002236346,"domain_scores_codex":[0.9997568,0.00004007572,0.00002590184,0.00008626531,0.00005936142,0.00003168501],"domain_scores_gemma":[0.9998599,0.00004470575,0.00002591803,0.00001801311,0.00002955329,0.00002195831],"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":[0.00162995,0.0003134005,0.04785668,0.0001039158,0.00005332996,0.0003388802,0.000217442,0.0006981893,0.92337,0.0001419617,0.0007407774,0.02453548],"study_design_scores_gemma":[0.0001688415,0.00333678,0.2314494,0.0000337027,0.0002583132,0.004743741,0.0003346529,0.01169833,0.7391682,0.0004722967,0.008302,0.00003375919],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909534,0.0007480775,0.006024467,0.0001076826,0.00001817971,0.0001187819,0.00108616,0.0001571665,0.0007860132],"genre_scores_gemma":[0.9898177,0.0003579274,0.005946709,0.000125003,0.00002805911,0.0002071124,0.002522445,0.00002092054,0.0009742024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001883857,"threshold_uncertainty_score":0.006302178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0566600850589305,"score_gpt":0.3161915275369681,"score_spread":0.2595314424780376,"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."}}