{"id":"W2939154568","doi":"10.1101/604397","title":"Profiling Myelodysplastic Syndromes by Mass Cytometry Demonstrates Abnormal Progenitor Cell Phenotype and Differentiation","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Stanford Cancer Institute; National Institutes of Health; Novartis Pharmaceuticals Corporation; Pfizer; Hamilton Health Sciences Foundation; Bill and Melinda Gates Foundation; U.S. Department of Defense","keywords":"Mass cytometry; Cytometry; Myelodysplastic syndromes; Progenitor cell; Flow cytometry; Cluster of differentiation; Bone marrow; Myeloid; Biology; CD44; Stem cell; Cell; Pathology; Phenotype; Immunology; Medicine; Cell biology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004086858,0.000261908,0.0002087073,0.001079762,0.0001891861,0.0003397979,0.0001624652,0.0001906422,0.0008264966],"category_scores_gemma":[0.0006138643,0.00008736426,0.0001197958,0.0003476677,0.0002564036,0.0001337323,0.0002875024,0.0002158022,0.0001931249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002249039,"about_ca_system_score_gemma":0.0001312241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003850477,"about_ca_topic_score_gemma":0.0006263544,"domain_scores_codex":[0.9997705,0.0000537685,0.00002385273,0.00005821245,0.00007038127,0.00002330821],"domain_scores_gemma":[0.9997706,0.00006723416,0.00005310647,0.00003393389,0.00004574795,0.00002934236],"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":[0.0002268509,0.00003559301,0.08497028,0.00004629481,0.00002130765,0.0001571241,0.000126495,0.0005226708,0.8995813,0.0002452031,0.00006584916,0.01400101],"study_design_scores_gemma":[0.00002875714,0.0008013324,0.2825327,0.00001744488,0.00006796206,0.002792572,0.0002780645,0.009772481,0.7008599,0.0005292807,0.002299808,0.00001976417],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932351,0.0002292344,0.006055454,0.0000223775,0.000002794741,0.0000310135,0.0001214924,0.00003813249,0.0002644046],"genre_scores_gemma":[0.9941112,0.0001007874,0.005409225,0.00001538801,0.000005092083,0.00002528446,0.0001613509,0.000004471109,0.0001671818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001079762,"threshold_uncertainty_score":0.00276494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01191910341129795,"score_gpt":0.2340284441356842,"score_spread":0.2221093407243863,"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."}}