{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.002581873,0.001286377,0.003882229,0.00190502,0.0003121803,0.0001777353,0.0005862616,0.000930934,0.00002025077],"category_scores_gemma":[0.01005384,0.001235336,0.000467141,0.005338432,0.001948318,0.001132412,0.001387837,0.002767004,0.00003330926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004219534,"about_ca_system_score_gemma":0.0007193752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003415343,"about_ca_topic_score_gemma":0.000008508802,"domain_scores_codex":[0.9890969,0.001131172,0.003713328,0.002470964,0.001958849,0.00162882],"domain_scores_gemma":[0.9654652,0.02982526,0.001308425,0.0009880642,0.0006755913,0.00173741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000409751,0.001569161,0.9552527,0.006144769,0.001140771,0.0005662132,0.001724024,0.0003095748,0.02873289,0.0001244378,0.0003458193,0.003679896],"study_design_scores_gemma":[0.01232912,0.00672911,0.90864,0.001861661,0.0009717189,0.0003336181,0.003812244,0.02038983,0.04419915,0.00005882494,0.0002443022,0.0004304329],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867644,0.003237063,0.00601602,0.0002218612,0.0005342267,0.002141957,0.0005141146,0.0001634483,0.0004069381],"genre_scores_gemma":[0.9808553,0.009349346,0.00818421,0.0004514194,0.0005356331,0.0002496207,0.0001200513,0.0002060732,0.00004838308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0466127,"threshold_uncertainty_score":0.9999888,"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."}}