{"id":"W4402581408","doi":"10.1080/15265161.2024.2388724","title":"Machine Learning-Generated Clinical Data as Collateral Research: A Global Neuroethical Analysis","year":2024,"lang":"en","type":"letter","venue":"The American Journal of Bioethics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"National Institute on Minority Health and Health Disparities; National Center for Advancing Translational Sciences; National Cancer Institute; National Institute on Aging; National Institutes of Health","keywords":"Collateral; Data science; Data analysis; Computer science; Artificial intelligence; Economics; Data mining; Finance","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":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01381303,0.0003129613,0.001400769,0.0005949084,0.0003143612,0.0002208215,0.001386241,0.0007710424,0.0001349726],"category_scores_gemma":[0.00519397,0.0001935343,0.0006054147,0.004295095,0.00430233,0.00007649068,0.0004283028,0.02536544,0.0002597081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002781032,"about_ca_system_score_gemma":0.004172141,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006852057,"about_ca_topic_score_gemma":0.0002799336,"domain_scores_codex":[0.9894875,0.00529313,0.001957166,0.0006130156,0.002020604,0.0006285533],"domain_scores_gemma":[0.9904385,0.00425642,0.001326785,0.001443857,0.002168445,0.0003659851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001544039,0.0001591307,0.01094112,0.0001614798,0.003879744,0.003370285,0.0009954328,0.00007857202,0.000008951852,0.00007540139,0.9329295,0.04585635],"study_design_scores_gemma":[0.00008132777,0.006333193,0.0004907103,0.000385201,0.005286309,0.001897449,0.001513971,0.006889059,0.00002614726,0.002466873,0.9743457,0.0002840392],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.05235918,0.001849427,0.0001009895,0.9434736,0.001601446,0.0002297031,0.0001196346,0.00003307998,0.000232986],"genre_scores_gemma":[0.2761345,0.009586441,0.0005738446,0.69242,0.01928832,0.000004086416,0.000693044,0.00008340697,0.001216382],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.2510535,"threshold_uncertainty_score":0.9997614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6938224854611319,"score_gpt":0.6219445264387992,"score_spread":0.07187795902233274,"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."}}