{"id":"W4309264259","doi":"10.1371/journal.pone.0272825","title":"A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Public Health Ontario; University of Toronto; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Compagnia di San Paolo","keywords":"Medicine; Counterfactual thinking; Type 2 diabetes; Diabetes mellitus; Body mass index; Internal medicine; Psychology; Endocrinology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004335944,0.0009280089,0.0008859565,0.002066088,0.0004794304,0.001155065,0.001613949,0.00122185,0.0024187],"category_scores_gemma":[0.02217864,0.0004667829,0.001446179,0.001040075,0.0006238162,0.001317601,0.0009871738,0.001277437,0.000280915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001002101,"about_ca_system_score_gemma":0.002075188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004409634,"about_ca_topic_score_gemma":0.004591695,"domain_scores_codex":[0.9967854,0.001394029,0.0002821954,0.0007466128,0.0006651051,0.0001267306],"domain_scores_gemma":[0.9818279,0.01493272,0.001108924,0.0007856367,0.001194447,0.0001502892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007504702,0.0005398484,0.02357107,0.0007197825,0.0005511364,0.00111927,0.001282379,0.3271881,0.005592216,0.02916686,0.004863256,0.6046556],"study_design_scores_gemma":[0.00006680048,0.00009832649,0.001765384,0.00005004088,0.00007801117,0.0001642974,0.0001102548,0.9797354,0.001602609,0.01468403,0.001619188,0.0000255929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02497767,0.000170695,0.9722477,0.0005276971,0.0000418089,0.00021506,0.0003604918,0.0005930431,0.0008659731],"genre_scores_gemma":[0.3485073,0.0001634891,0.6488564,0.0002345465,0.00008069312,0.0004872336,0.0008254063,0.0000466178,0.0007982222],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004409634,"threshold_uncertainty_score":0.02293092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1495686149418002,"score_gpt":0.3655659205258006,"score_spread":0.2159973055840004,"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."}}