{"id":"W2141817946","doi":"10.1155/2003/262918","title":"Risk Biomarker Assessment for Breast Cancer Progression: Replication Precision of Nuclear Morphometry","year":2003,"lang":"en","type":"article","venue":"Analytical Cellular Pathology","topic":"AI in cancer detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"National Cancer Institute; National Institutes of Health","keywords":"Feulgen stain; Pathology; Breast cancer; Histopathology; Stain; Nuclear DNA; Biology; Medicine; Cancer; Staining; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001115924,0.0001385735,0.0002572169,0.0001671312,0.0001188229,0.00002954846,0.0003983063,0.0001744211,0.0001273314],"category_scores_gemma":[0.0001757622,0.0001201355,0.0001351941,0.0006028641,0.0001211787,0.000212987,0.0001114,0.0001603009,0.00001125914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001290643,"about_ca_system_score_gemma":0.00009019279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001253824,"about_ca_topic_score_gemma":0.000001488684,"domain_scores_codex":[0.9981015,0.000272197,0.0003894143,0.0006898243,0.0002750169,0.0002720188],"domain_scores_gemma":[0.9981908,0.0001723933,0.0002861308,0.001021774,0.0002347794,0.00009417026],"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":[0.0001663319,0.0006896195,0.02423386,0.0001225877,0.0001613919,0.00004681207,0.0001462287,0.0002203656,0.09883353,0.1204982,0.003178121,0.751703],"study_design_scores_gemma":[0.002358404,0.001087323,0.1755954,0.000110988,0.0002950964,0.0003787977,0.00006278446,0.7113644,0.04911272,0.0272996,0.03151366,0.0008207324],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07015672,0.0002363948,0.9270161,0.0008400753,0.0004026805,0.0004136974,0.00003146305,0.00007604755,0.0008268176],"genre_scores_gemma":[0.9183751,0.00007903786,0.0812223,0.0001146772,0.00004335992,0.00007290986,0.000004172355,0.00001778921,0.00007067499],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8482184,"threshold_uncertainty_score":0.4898983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0218421462256762,"score_gpt":0.3289193682651577,"score_spread":0.3070772220394815,"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."}}