{"id":"W4310823879","doi":"10.2196/preprints.44332","title":"An Actionable Expert-System Algorithm to Support Nurse-Led Cancer Survivorship Care: Algorithm Development Study (Preprint)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Cancer survivorship and care","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Princess Margaret Cancer Centre; Trillium Health Centre; Niagara Health System; Queen Elizabeth II Health Sciences Centre; McMaster University; University of Waterloo; Toronto General Hospital; University of Toronto; University Health Network","funders":"","keywords":"Algorithm; Survivorship curve; Autonomy; Psychosocial; Nursing; Reimbursement; Medicine; Health care; Family medicine; Psychology; Cancer; Computer science; Psychiatry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01506541,0.0007997813,0.0007357768,0.0009696974,0.0006633236,0.001758757,0.001521338,0.001059064,0.004006035],"category_scores_gemma":[0.05889872,0.0003930804,0.0007048542,0.0007391199,0.0003553314,0.001063413,0.0009912088,0.0009325436,0.0005590197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001790954,"about_ca_system_score_gemma":0.004734683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01012931,"about_ca_topic_score_gemma":0.008307293,"domain_scores_codex":[0.9941221,0.003931864,0.0005422395,0.0006098717,0.0006482903,0.0001455905],"domain_scores_gemma":[0.9446004,0.04381299,0.001088869,0.001232303,0.008883189,0.000382186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002186894,0.002915602,0.05905744,0.001151279,0.000576312,0.0004204946,0.001037383,0.462276,0.002899909,0.00601989,0.01002911,0.4514297],"study_design_scores_gemma":[0.0003578218,0.0003726177,0.002447976,0.00008990533,0.0001233791,0.00006835692,0.0002133599,0.9905162,0.002540213,0.001389799,0.001860858,0.00001939086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3269437,0.0002754434,0.6535383,0.0008258885,0.0001378262,0.008049793,0.00184638,0.002326421,0.006056192],"genre_scores_gemma":[0.3097031,0.00009453866,0.6842096,0.0001523432,0.00001712337,0.003057784,0.001762944,0.00006923955,0.0009333418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01506541,"threshold_uncertainty_score":0.07967448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02788631372877732,"score_gpt":0.3373136239831483,"score_spread":0.309427310254371,"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."}}