{"id":"W2109235562","doi":"10.1016/j.ultrasmedbio.2004.11.004","title":"Monitoring the formation of thermal lesions with heat-induced echo-strain imaging: A feasibility study","year":2005,"lang":"en","type":"article","venue":"Ultrasound in Medicine & Biology","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":99,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"U.S. Public Health Service","keywords":"Echo (communications protocol); Strain (injury); Ultrasonic sensor; Materials science; Ultrasound; SIGNAL (programming language); Lesion; Biomedical engineering; Gross examination; Nuclear magnetic resonance; Medicine; Pathology; Radiology; Physics; Anatomy; Computer science","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":[0.001127878,0.0002429266,0.0005104694,0.0002695688,0.0001293071,0.000008194515,0.0001971954,0.00006718836,0.00005767671],"category_scores_gemma":[0.0006232077,0.0001308754,0.0000720542,0.0005521154,0.0005100404,0.0001411599,0.00002064833,0.0005628844,0.000004698824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009531147,"about_ca_system_score_gemma":0.0000793879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005691535,"about_ca_topic_score_gemma":0.0001542317,"domain_scores_codex":[0.9981551,0.0002539311,0.0005898136,0.0003447958,0.0002515643,0.0004047923],"domain_scores_gemma":[0.9983347,0.0007607673,0.0001286215,0.0005285799,0.0001407976,0.0001065318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001995341,0.0004716841,0.6795262,0.00001783931,0.00003485998,0.000002099614,0.005420113,0.00001471816,0.301025,0.00001280932,0.0000260108,0.01324912],"study_design_scores_gemma":[0.004946811,0.002745001,0.9668668,0.0002968246,0.0001953814,0.0002720694,0.0192404,0.00008004742,0.003740531,0.0001266032,0.001288744,0.0002008115],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929888,0.0005058912,0.0005537734,0.003080366,0.0001629967,0.0008739953,0.000007208463,0.00006196802,0.001765051],"genre_scores_gemma":[0.9982574,0.00007163781,0.0008153805,0.0003265141,0.0004187738,0.00004557279,0.00002728032,0.00001948488,0.00001794963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2972845,"threshold_uncertainty_score":0.5336943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03414338223579596,"score_gpt":0.3285757170225301,"score_spread":0.2944323347867342,"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."}}