{"id":"W4385820722","doi":"10.2196/44332","title":"An Actionable Expert-System Algorithm to Support Nurse-Led Cancer Survivorship Care: Algorithm Development Study","year":2023,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Cancer survivorship and care","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Calgary; Princess Margaret Cancer Centre; Trillium Health Centre; Niagara Health System; Queen Elizabeth II Health Sciences Centre; McMaster University; University of Waterloo; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Algorithm; Survivorship curve; Psychosocial; Autonomy; Nursing; Reimbursement; Health care; Medicine; MEDLINE; Family medicine; Cancer; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0003964448,0.000494344,0.0007224223,0.0004153903,0.0003276311,0.00009664796,0.0003312315,0.0002022378,0.001323594],"category_scores_gemma":[0.000006955938,0.0004621103,0.0001527971,0.001515759,0.00003978725,0.0002233824,0.00008132267,0.0003253588,0.0003963146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002396903,"about_ca_system_score_gemma":0.001450577,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02620288,"about_ca_topic_score_gemma":0.05071799,"domain_scores_codex":[0.9961558,0.0001317294,0.0005700676,0.001030881,0.001180816,0.0009307099],"domain_scores_gemma":[0.9977569,0.00004024437,0.0001258671,0.0007893436,0.0005594873,0.0007281636],"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":[0.0002679171,0.0004401528,0.04377449,0.0003068826,0.0003694167,0.0003277035,0.07291324,0.00007042766,0.0004911411,0.0000072255,0.02057181,0.8604596],"study_design_scores_gemma":[0.004708737,0.001628784,0.1603968,0.0005957953,0.0002141092,0.00002943066,0.2054039,0.001214429,0.006132814,0.00000255228,0.6182079,0.001464829],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713143,0.001549404,0.001279945,0.001528545,0.01204176,0.006282513,0.0005070659,0.002169563,0.003326943],"genre_scores_gemma":[0.9632221,0.0001044675,0.001381589,0.001342098,0.002490905,0.01680994,0.0003519828,0.0002316892,0.01406519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8589948,"threshold_uncertainty_score":0.999783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02941776560691015,"score_gpt":0.3641041368389872,"score_spread":0.334686371232077,"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."}}