{"id":"W4413223111","doi":"10.3390/curroncol32080450","title":"Septic Shock in Hematological Malignancies: Role of Artificial Intelligence in Predicting Outcomes","year":2025,"lang":"en","type":"review","venue":"Current Oncology","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Septic shock; Intensive care medicine; Sepsis; Triage; Population; Artificial intelligence; Immunology; Medical emergency; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.004605343,0.000706384,0.0008127015,0.002217948,0.000267136,0.002887827,0.000781021,0.0008506204,0.0009292556],"category_scores_gemma":[0.0251842,0.0001817855,0.0006625316,0.001578085,0.0008174758,0.001547868,0.001198997,0.00269126,0.0003171306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008449539,"about_ca_system_score_gemma":0.001020888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003024092,"about_ca_topic_score_gemma":0.002072304,"domain_scores_codex":[0.9980049,0.001150035,0.00018313,0.000220262,0.0003551592,0.00008663839],"domain_scores_gemma":[0.988351,0.008540617,0.001103529,0.0004307102,0.001110555,0.0004636066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000636188,0.0003242888,0.2581713,0.001255899,0.0009126071,0.0003287427,0.0004285322,0.05141047,0.0007881485,0.01117531,0.01517376,0.6593948],"study_design_scores_gemma":[0.0001755552,0.001230544,0.2100631,0.005550948,0.001165107,0.001264878,0.001194049,0.5362773,0.002646213,0.1845621,0.05546974,0.0004006326],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.3741363,0.2535115,0.217864,0.1057879,0.003000285,0.0004126692,0.00400062,0.001456765,0.03983006],"genre_scores_gemma":[0.922996,0.03671116,0.03299435,0.003381757,0.001584524,0.0001527821,0.001263912,0.00005546836,0.0008600179],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004605343,"threshold_uncertainty_score":0.02435565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3370143190472422,"score_gpt":0.5142010942703671,"score_spread":0.1771867752231249,"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."}}