{"id":"W4387414769","doi":"10.1007/978-3-031-44336-7_5","title":"Examining the Effects of Slice Thickness on the Reproducibility of CT Radiomics for Patients with Colorectal Liver Metastases","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Resampling; Reproducibility; Medicine; Radiomics; Proportional hazards model; Colorectal cancer; Artificial intelligence; Robustness (evolution); Radiology; Feature (linguistics); Data set; Computer science; Pattern recognition (psychology); Statistics; Internal medicine; Cancer; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.003214133,0.0002574176,0.0005647066,0.0001928528,0.0001472142,0.00002891217,0.0006089115,0.00006900623,0.000003593446],"category_scores_gemma":[0.006266608,0.0001255547,0.0001044554,0.0003125001,0.001392716,0.00004206657,0.0002165403,0.0007261975,7.880643e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007292032,"about_ca_system_score_gemma":0.0002409081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003656565,"about_ca_topic_score_gemma":0.000008158471,"domain_scores_codex":[0.9975943,0.00008006138,0.0003669101,0.0009680209,0.0007291563,0.0002615795],"domain_scores_gemma":[0.990763,0.007284265,0.0003691934,0.001251433,0.0002748798,0.00005720274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001118854,0.000469337,0.01970107,0.003203954,0.0004797163,0.0001159742,0.004964869,0.01517628,0.001624698,0.006808359,0.0002221766,0.9461147],"study_design_scores_gemma":[0.01296621,0.02391591,0.2062529,0.02594159,0.00226297,0.0003334192,0.00002076645,0.6285437,0.05948624,0.03557317,0.002066987,0.002636177],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5794138,0.0003617483,0.4123721,0.001679576,0.001840552,0.003824292,0.00003394732,0.00006899135,0.0004050367],"genre_scores_gemma":[0.9836717,0.00003613417,0.01492927,0.0009822801,0.000213313,0.00002539499,0.00001490948,0.00004643328,0.00008059439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9434785,"threshold_uncertainty_score":0.7502168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01886707748835155,"score_gpt":0.2612233830534904,"score_spread":0.2423563055651388,"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."}}