{"id":"W2078950767","doi":"10.1117/12.2043729","title":"Identifying MRI markers to evaluate early treatment-related changes post-laser ablation for cancer pain management","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute","keywords":"Magnetic resonance imaging; Medicine; Ablation; Real-time MRI; Radiology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006892268,0.0004870094,0.0005950179,0.001505692,0.0001400782,0.0008679177,0.0002676374,0.0007490776,0.0008510866],"category_scores_gemma":[0.001676072,0.0002904146,0.0003316828,0.0007498744,0.0002297611,0.0005313802,0.0002400253,0.0004937551,0.0004144564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001803164,"about_ca_system_score_gemma":0.0002732346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008108832,"about_ca_topic_score_gemma":0.002022961,"domain_scores_codex":[0.9997763,0.00006568069,0.00002113953,0.00003494228,0.00006359826,0.00003829097],"domain_scores_gemma":[0.9993962,0.0001373508,0.0002647259,0.00004113994,0.0001184549,0.00004218307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003627556,0.0009002936,0.06695061,0.00189533,0.0004625393,0.0007867294,0.0003922512,0.009533154,0.5981867,0.0005060071,0.00189036,0.3148684],"study_design_scores_gemma":[0.0002050337,0.006861841,0.5916764,0.0005151131,0.00136488,0.003648478,0.0008310642,0.07439098,0.3077612,0.001177767,0.01136143,0.0002057778],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9100547,0.01833589,0.06617608,0.0004271196,0.0001654941,0.0004488201,0.0007384582,0.0005384355,0.003114946],"genre_scores_gemma":[0.9368078,0.008356662,0.05157816,0.0002503166,0.0001494661,0.0003983519,0.0007119367,0.0001096508,0.00163763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001505692,"threshold_uncertainty_score":0.003645003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01632373847643996,"score_gpt":0.2878921413997246,"score_spread":0.2715684029232847,"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."}}