{"id":"W2912814480","doi":"10.1002/cjp2.127","title":"The use of digital pathology and image analysis in clinical trials","year":2019,"lang":"en","type":"article","venue":"The Journal of Pathology Clinical Research","topic":"AI in cancer detection","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Barts and The London School of Medicine and Dentistry; Medical Research Council; Newcastle upon Tyne Hospitals NHS Foundation Trust; Queen's University; Cancer Research UK; Queen's University Belfast; University of Oxford; University of Southampton; Newcastle University; National Cancer Research Institute; Bristol-Myers Squibb; UK Research and Innovation; University of Leeds; National Institute for Health and Care Research","keywords":"Digital pathology; Digital image analysis; Computer science; Medical physics; Clinical trial; Data science; Key (lock); Digital image; Pathology; Medicine; Artificial intelligence; Image processing; Image (mathematics); Computer vision","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1430496,0.0001022056,0.001037052,0.0004246903,0.0001097009,0.0001403821,0.001553764,0.0002669116,0.00001352123],"category_scores_gemma":[0.04919907,0.00005127989,0.0004802167,0.001209453,0.001756848,0.0004844748,0.0008393769,0.002262569,0.00002983121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003861695,"about_ca_system_score_gemma":0.0003015576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008266292,"about_ca_topic_score_gemma":0.00003417172,"domain_scores_codex":[0.9730155,0.02195872,0.003493275,0.0003393754,0.0007535703,0.0004394879],"domain_scores_gemma":[0.9023555,0.09435867,0.001335097,0.001030406,0.0007769197,0.0001434169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002726243,0.0005061388,0.572199,0.00001566047,0.0006470263,0.0009176435,0.0006707614,0.0002275954,0.001304739,0.001557654,0.002158177,0.4170693],"study_design_scores_gemma":[0.004770603,0.008185878,0.9330549,0.00007304479,0.00034786,0.001318377,0.0004401585,0.01792648,0.0002479777,0.02851974,0.004865577,0.0002494542],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704572,0.0004674229,0.02297873,0.005061409,0.0007033745,0.0002522807,0.000005274046,0.000005207377,0.0000690892],"genre_scores_gemma":[0.9934434,0.00409644,0.002058334,0.0001172804,0.0001691946,0.000002327656,2.670253e-7,0.000006017456,0.0001067038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4168199,"threshold_uncertainty_score":0.9829861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.517263181926019,"score_gpt":0.5866856574035789,"score_spread":0.06942247547755998,"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."}}