{"id":"W3154535333","doi":"10.2214/ajr.21.25456","title":"Assessment of Renal Cell Carcinoma by Texture Analysis in Clinical Practice: A Six-Site, Six-Platform Analysis of Reliability","year":2021,"lang":"en","type":"article","venue":"American Journal of Roentgenology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Kurtosis; Medicine; Intraclass correlation; Software; Pearson product-moment correlation coefficient; Texture (cosmology); Reproducibility; Artificial intelligence; Pattern recognition (psychology); Skewness; Segmentation; Correlation; Image texture; Renal cell carcinoma; Nuclear medicine; Statistics; Image segmentation; Computer science; Pathology; Mathematics; Image (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":[0.002623033,0.000176909,0.002266234,0.0009977898,0.00002643062,0.000007764127,0.0002237267,0.0001196279,0.0002250876],"category_scores_gemma":[0.002523441,0.0001451284,0.0009780984,0.003597171,0.0005958166,0.00009793245,0.00008705765,0.001302684,5.071266e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001604528,"about_ca_system_score_gemma":0.0006463646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005740245,"about_ca_topic_score_gemma":0.00008298284,"domain_scores_codex":[0.9958781,0.0006799942,0.002153571,0.0003497219,0.0006229107,0.0003157218],"domain_scores_gemma":[0.9945799,0.001108924,0.002652286,0.0005154387,0.0008758187,0.0002676242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003515613,0.001316142,0.9667761,0.00003918249,0.00474059,0.0004971465,0.0002892713,0.004622145,0.007588374,0.00002154698,0.000398285,0.01335964],"study_design_scores_gemma":[0.001906289,0.002318462,0.9571776,0.00003150803,0.01507302,0.0002066879,0.001702218,0.01911745,0.0004264523,0.00001331602,0.001910151,0.000116807],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864359,0.0007628154,0.008527362,0.003313644,0.000102504,0.00007202726,0.00001825708,0.000003778962,0.0007637183],"genre_scores_gemma":[0.9785141,0.0005144484,0.0200807,0.0007161865,0.00006299437,0.00000115967,0.00004958349,0.00001362962,0.00004719877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0144953,"threshold_uncertainty_score":0.5918167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00918467161824805,"score_gpt":0.3579846007332723,"score_spread":0.3487999291150243,"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."}}