{"id":"W3193353579","doi":"10.3390/curroncol28040275","title":"A Machine Learning Approach to Predict Stress Hormones and Inflammatory Markers Using Illness Perception and Quality of Life in Breast Cancer Patients","year":2021,"lang":"en","type":"article","venue":"Current Oncology","topic":"Cancer survivorship and care","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Breast cancer; Adrenocorticotropic hormone; Erythrocyte sedimentation rate; Psychosocial; Quality of life (healthcare); Hormone; Internal medicine; Oncology; Fibrinogen; Cancer; Psychiatry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002109054,0.0005557193,0.0007310277,0.001822501,0.0003295936,0.0007598085,0.0005574384,0.0005920279,0.001020462],"category_scores_gemma":[0.005188632,0.000236624,0.0006994538,0.0009896986,0.0002302989,0.0003466908,0.000356616,0.0009963779,0.000253029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006212969,"about_ca_system_score_gemma":0.001073281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006997556,"about_ca_topic_score_gemma":0.004982409,"domain_scores_codex":[0.9993864,0.0003723496,0.00005592507,0.00007505665,0.00005708179,0.00005324816],"domain_scores_gemma":[0.9971131,0.002292451,0.0001912361,0.00006890181,0.0002357663,0.00009859982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001217473,0.001855011,0.5998258,0.00008709711,0.0007054546,0.0002230464,0.0002944128,0.1830636,0.001415187,0.0008491564,0.001920997,0.2085428],"study_design_scores_gemma":[0.00004628571,0.0004445715,0.05272442,0.00001848305,0.00005699661,0.00008785336,0.0001198806,0.9444061,0.0003604467,0.001410534,0.0003026332,0.00002177144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8936689,0.0006864701,0.1012155,0.001334142,0.00007598396,0.000245469,0.0009252727,0.000396061,0.001452293],"genre_scores_gemma":[0.9794087,0.00011986,0.01919815,0.00007889524,0.00004586313,0.0001692105,0.0005808813,0.000008767559,0.0003895669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006997556,"threshold_uncertainty_score":0.01391363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05980492275936427,"score_gpt":0.3581576664171602,"score_spread":0.298352743657796,"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."}}