{"id":"W2908999604","doi":"10.1182/blood-2018-99-113403","title":"Developing Applicable and Cost-Efficient Screens for Early Detection of AML","year":2018,"lang":"en","type":"article","venue":"Blood","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"","keywords":"Myeloid leukemia; Leukemia; Myeloid; Computational biology; Biology; Medicine; Bioinformatics; Oncology; Internal medicine","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":[0.002024964,0.0008998223,0.00104514,0.001480632,0.0002231166,0.00128308,0.001186362,0.0008765616,0.001204858],"category_scores_gemma":[0.00278579,0.000462945,0.0007661569,0.0007327547,0.0004109292,0.0005853857,0.0015227,0.001045017,0.001004464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006639327,"about_ca_system_score_gemma":0.0007636502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007023257,"about_ca_topic_score_gemma":0.001159157,"domain_scores_codex":[0.997387,0.0005220752,0.0001973371,0.0004377517,0.001221194,0.0002346358],"domain_scores_gemma":[0.9979326,0.0007906294,0.0003640186,0.000290749,0.0004790856,0.0001429521],"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":[0.0001302592,0.0002102284,0.008321967,0.0003373164,0.00007226759,0.0001620029,0.00004359605,0.006761758,0.907173,0.001065706,0.0006797626,0.07504212],"study_design_scores_gemma":[0.00003083078,0.0003938858,0.006816451,0.00003700315,0.00009050236,0.000436032,0.00007338071,0.06242114,0.9224374,0.001076731,0.006141506,0.00004517312],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3921915,0.002134074,0.59115,0.001166173,0.0001174615,0.001315566,0.003325696,0.005474161,0.003125391],"genre_scores_gemma":[0.5328704,0.001237179,0.4598221,0.0004071473,0.00003663901,0.0005420053,0.002840865,0.0002333796,0.002010277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002024964,"threshold_uncertainty_score":0.01070917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03616010690561902,"score_gpt":0.3153150669595968,"score_spread":0.2791549600539778,"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."}}