{"id":"W4377027740","doi":"10.1177/22925503231176010","title":"An Approach to “Big Data”","year":2023,"lang":"en","type":"article","venue":"Plastic Surgery","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Impact; McMaster University","funders":"","keywords":"Big data; Computer science; Data science; Data mining","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002166226,0.0001542585,0.000322672,0.0003539897,0.0006528112,0.00001454774,0.0005076175,0.0001805435,0.0002069406],"category_scores_gemma":[0.01527309,0.000144501,0.00003681545,0.001090548,0.00004149809,0.0001669975,0.000322715,0.0004522044,0.01397536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008163723,"about_ca_system_score_gemma":0.0005639434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001009794,"about_ca_topic_score_gemma":0.0005157635,"domain_scores_codex":[0.9969075,0.0005377608,0.0006845339,0.0006018631,0.0003615879,0.0009067034],"domain_scores_gemma":[0.9672796,0.03077607,0.0001066523,0.001224316,0.0001441871,0.0004691602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009848865,0.0001270235,0.5983037,0.0004367184,0.00001891984,0.00003435128,0.003669821,0.0007378021,0.0001593896,0.0008300659,0.3591379,0.03644583],"study_design_scores_gemma":[0.0001385459,0.00009656046,0.4238893,0.0008358423,0.00005208182,0.000006614734,0.04042667,0.1942292,0.0001302167,0.001068227,0.3378611,0.001265687],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9564047,0.00003209507,0.00660777,0.0009067756,0.03081615,0.0005809399,0.0002966271,0.001066511,0.003288381],"genre_scores_gemma":[0.9949069,0.00001686636,0.0002446517,0.000602001,0.002874928,0.0002305887,0.0007002769,0.00005567648,0.000368133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1934914,"threshold_uncertainty_score":0.9930217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6083145185833758,"score_gpt":0.510966684242691,"score_spread":0.09734783434068484,"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."}}