{"id":"W3117986169","doi":"10.1002/cyto.b.21983","title":"Exploring blast composition in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms: <scp>CD45RA</scp> and <scp>CD371</scp> improve diagnostic value of flow cytometry through assessment of myeloblast heterogeneity and stem cell aberrancy","year":2020,"lang":"en","type":"article","venue":"Cytometry Part B Clinical Cytometry","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services; University of Calgary","funders":"","keywords":"Myelodysplastic syndromes; Immunophenotyping; Population; CD117; Medicine; International Prognostic Scoring System; Myeloproliferative neoplasm; Minimal residual disease; Myeloid; Bone marrow; Immunology; Stem cell; Flow cytometry; Myelofibrosis; Pathology; Internal medicine; Biology; CD34; Genetics","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":[0.0004010482,0.0002905988,0.0001772722,0.001493641,0.0001728011,0.0001931224,0.0001294491,0.0002574214,0.001305206],"category_scores_gemma":[0.0006507334,0.00009592461,0.00007805094,0.0003809807,0.0002776321,0.0001515581,0.0002618603,0.0002180877,0.0002609638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001731019,"about_ca_system_score_gemma":0.0001116495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004439178,"about_ca_topic_score_gemma":0.0007055915,"domain_scores_codex":[0.9998666,0.00003236033,0.00001933151,0.00002970585,0.00003641491,0.00001557881],"domain_scores_gemma":[0.9997883,0.00008113334,0.00004318636,0.00001021163,0.00004129636,0.0000359217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009209005,0.00008471205,0.5994778,0.0001260733,0.00003948695,0.001641099,0.0003450233,0.0004719387,0.3671105,0.000165818,0.0001668508,0.02944978],"study_design_scores_gemma":[0.00006711431,0.0007059926,0.854053,0.00002248189,0.00006333183,0.01172905,0.0002003398,0.00297115,0.1287697,0.0002322311,0.001177625,0.000007979135],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99809,0.0002560918,0.001127577,0.00001041521,0.000001546714,0.00002508722,0.00007111657,0.00001711019,0.0004009774],"genre_scores_gemma":[0.9972574,0.0001376426,0.00220117,0.00001584156,0.000003453092,0.00001757328,0.0002103833,0.000003632007,0.0001530529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001493641,"threshold_uncertainty_score":0.004366279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09570394698014477,"score_gpt":0.3511542081561805,"score_spread":0.2554502611760358,"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."}}