{"id":"W4399826484","doi":"10.1177/10732748241264704","title":"Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Cancer Control","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Toronto Metropolitan University","keywords":"Medicine; Artificial intelligence; Context (archaeology); Generative grammar; Disease; Machine learning; Computer science; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001777562,0.0004934053,0.0008286727,0.0004186211,0.0003536594,0.002143564,0.001199677,0.001334584,0.001644533],"category_scores_gemma":[0.003879227,0.0003520796,0.0006147065,0.0003400088,0.003586928,0.002265526,0.001625006,0.003311326,0.0003224436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001480728,"about_ca_system_score_gemma":0.0008263304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00126384,"about_ca_topic_score_gemma":0.000875622,"domain_scores_codex":[0.999234,0.0003159126,0.00002964066,0.0001239557,0.0002457633,0.00005083447],"domain_scores_gemma":[0.9983485,0.001149537,0.0001017951,0.0002342275,0.0001129675,0.00005303138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006753238,0.00005998806,0.0008315121,0.0001707221,0.00007938476,0.0001080109,0.0004206529,0.2789991,0.003132597,0.6567975,0.002038381,0.05729463],"study_design_scores_gemma":[0.00002262814,0.00008737321,0.0002600248,0.00005850383,0.00002317107,0.00007432124,0.00005943154,0.4653086,0.0009536308,0.5207262,0.01239714,0.00002903578],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01658097,0.004542098,0.9486969,0.008228702,0.0002200352,0.00007492808,0.00005627911,0.0005196971,0.02108045],"genre_scores_gemma":[0.8283697,0.004948525,0.1558225,0.001966776,0.0004525848,0.0002367948,0.00008678266,0.000167794,0.007948495],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.002143564,"threshold_uncertainty_score":0.0107435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04148395327002339,"score_gpt":0.3592394429347402,"score_spread":0.3177554896647168,"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."}}