{"id":"W4412163849","doi":"10.1158/1557-3265.aimachine-b038","title":"Abstract B038: Machine learning used to validate neutrophil classification in Triple Negative Breast Cancer patients","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Inflammatory Biomarkers in Disease Prognosis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cancer; Medicine; Triple-negative breast cancer; Breast cancer; Triple negative; Oncology; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002005316,0.0007058309,0.0006539499,0.001794137,0.0004186633,0.001074582,0.0007003362,0.0009884998,0.004403423],"category_scores_gemma":[0.005937854,0.0001508695,0.0005449033,0.0007176279,0.0002391288,0.0003272962,0.0007013879,0.0009123742,0.002636626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004951094,"about_ca_system_score_gemma":0.0006040991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002738538,"about_ca_topic_score_gemma":0.00246399,"domain_scores_codex":[0.9990035,0.0003012425,0.000103527,0.0003039792,0.000187015,0.0001008855],"domain_scores_gemma":[0.9980549,0.0008541423,0.0002250588,0.0001524118,0.0005273359,0.0001862109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003812003,0.0008879941,0.6691337,0.0006039517,0.0006132502,0.0005616458,0.0001997999,0.02509023,0.02035473,0.0006953918,0.03820186,0.2398455],"study_design_scores_gemma":[0.0004009206,0.002034665,0.2866796,0.0004055605,0.0004543023,0.001669978,0.0005451561,0.6490664,0.0340175,0.003651472,0.02097035,0.0001040133],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.926735,0.002301378,0.03192724,0.001521212,0.0003434051,0.0004263741,0.02796434,0.003630444,0.005150599],"genre_scores_gemma":[0.9497202,0.0002778239,0.02293851,0.0004540868,0.0001114004,0.0003796877,0.0240557,0.0001432123,0.001919472],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.004403423,"threshold_uncertainty_score":0.01473087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1922856153902882,"score_gpt":0.5138475830742069,"score_spread":0.3215619676839186,"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."}}