{"id":"W4297464425","doi":"10.3390/diagnostics12102351","title":"Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View","year":2022,"lang":"en","type":"article","venue":"Diagnostics","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lawson Health Research Institute; University of Waterloo; Western University","funders":"","keywords":"Annotation; Computer science; Artificial intelligence; Lung ultrasound; Machine learning; Ultrasound; Medicine; Radiology","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":[0.0003628711,0.0001174977,0.0002444044,0.00005528887,0.0002784306,0.00001542457,0.0001106789,0.00003460915,0.0005645493],"category_scores_gemma":[0.002822293,0.0001083747,0.00003353002,0.0005227207,0.00009579443,0.00005182259,0.00004770338,0.0003642042,0.00001657048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007103304,"about_ca_system_score_gemma":0.00009139262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007384613,"about_ca_topic_score_gemma":0.00001355101,"domain_scores_codex":[0.9986537,0.00009139748,0.0004312546,0.0002530319,0.0003784692,0.0001921187],"domain_scores_gemma":[0.9960327,0.003256071,0.0002363924,0.0002769978,0.0001058088,0.00009200552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002724649,0.004475175,0.7775635,0.000664093,0.0003134104,0.00004727834,0.001790586,0.01181456,0.05981243,0.001426705,0.1410227,0.0007970803],"study_design_scores_gemma":[0.01187869,0.009741095,0.2005282,0.001309207,0.003256292,0.0008350498,0.002561004,0.1431162,0.05584192,0.0005310401,0.5683594,0.002041894],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771422,0.001589591,0.0128543,0.0007828872,0.00009869498,0.0009761124,0.005775406,0.0004220861,0.0003587373],"genre_scores_gemma":[0.9743778,0.0003276991,0.002192079,0.0005010811,0.00002701536,0.0001811881,0.02230865,0.00002580204,0.00005873653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5770353,"threshold_uncertainty_score":0.6181419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01425859071557397,"score_gpt":0.3144214119019061,"score_spread":0.3001628211863321,"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."}}