{"id":"W2937086749","doi":"10.1097/mlr.0000000000001089","title":"A PRO-cision Medicine Methods Toolkit to Address the Challenges of Personalizing Cancer Care Using Patient-Reported Outcomes","year":2019,"lang":"en","type":"article","venue":"Medical Care","topic":"Cancer survivorship and care","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Cancer Institute","keywords":"MEDLINE; Personalized medicine; Precision medicine; Medicine; Patient care; Medical education; Nursing; Bioinformatics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000529478,0.0002600054,0.0007852758,0.0001362446,0.00008318182,0.000007709423,0.0002553881,0.0002169749,0.001366734],"category_scores_gemma":[0.0008049613,0.0001478999,0.0002069433,0.0003477853,0.0001342576,0.00004495225,0.0001768691,0.0004002238,0.000005087758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002728243,"about_ca_system_score_gemma":0.000455967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003961364,"about_ca_topic_score_gemma":0.002326079,"domain_scores_codex":[0.9967085,0.000197991,0.0005473882,0.0004681281,0.001720628,0.0003573881],"domain_scores_gemma":[0.9977921,0.0003001372,0.0001859111,0.0006276672,0.0007196128,0.0003745723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004327377,0.00005498649,0.170503,0.002557394,0.0003138894,0.0001170258,0.2727819,0.00001784457,0.001628803,0.0002189846,0.0004954822,0.5508779],"study_design_scores_gemma":[0.005931058,0.002337195,0.1189009,0.0106225,0.001309778,0.0001502432,0.7557647,0.000328215,0.005868822,0.0000447726,0.09789329,0.0008485466],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.924549,0.05808412,0.0001335159,0.009799578,0.002011273,0.001239923,0.00002568517,0.00006295623,0.004093945],"genre_scores_gemma":[0.996403,0.0002857232,0.0004204875,0.002302215,0.0003341619,0.00009436428,0.00002029477,0.00004290817,0.00009687724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5500294,"threshold_uncertainty_score":0.9995462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0908532731547171,"score_gpt":0.4284644906315793,"score_spread":0.3376112174768622,"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."}}