{"id":"W4411398842","doi":"10.1177/10732748251349919","title":"Generative AI - Assisted Adaptive Cancer Therapy","year":2025,"lang":"en","type":"review","venue":"Cancer Control","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Toronto Metropolitan University","keywords":"Medicine; Context (archaeology); Artificial intelligence; Cancer therapy; Precision medicine; CLARITY; Leverage (statistics); Machine learning; Cancer; Computer science; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003618838,0.000919461,0.00415923,0.0002093206,0.0001467499,0.00005572833,0.0006934162,0.000659187,0.00164833],"category_scores_gemma":[0.0004462732,0.0006191357,0.0009263642,0.000458909,0.0002083632,0.0000646663,0.00008419056,0.0008962023,0.0000584403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006175098,"about_ca_system_score_gemma":0.001797538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004555958,"about_ca_topic_score_gemma":0.00008452332,"domain_scores_codex":[0.9964677,0.0007454928,0.001052109,0.000828217,0.0002932612,0.0006132395],"domain_scores_gemma":[0.9953949,0.0026178,0.0007595442,0.0007653958,0.0003039299,0.0001583794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004768644,0.0001323906,0.000002913853,0.01183467,0.002559438,0.0000234224,0.00007953687,3.41652e-7,0.000002271751,0.04996182,0.03213516,0.9032204],"study_design_scores_gemma":[0.001551356,0.00008682987,0.000001347327,0.01889246,0.002422218,0.000009156775,0.000008430807,0.00007065145,0.000008226472,0.02413016,0.9520336,0.0007855888],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[5.266534e-7,0.9851146,0.005089597,0.0005079461,0.0007639911,0.003065537,0.001171437,0.0001771052,0.004109279],"genre_scores_gemma":[0.00001099976,0.9816338,0.0006305826,0.001613771,0.0007094482,0.008283716,0.00002759987,0.0001017022,0.006988422],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9198984,"threshold_uncertainty_score":0.999626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.142185179195484,"score_gpt":0.4370320234793442,"score_spread":0.2948468442838602,"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."}}