{"id":"W4401391623","doi":"10.3389/fnut.2024.1390751","title":"Predicting and comparing the long-term impact of lifestyle interventions on individuals with eating disorders in active population: a machine learning evaluation","year":2024,"lang":"en","type":"article","venue":"Frontiers in Nutrition","topic":"Eating Disorders and Behaviors","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Iran National Science Foundation; National Science Foundation","keywords":"Psychological intervention; Term (time); Psychology; Eating disorders; Population; Computer science; Gerontology; Applied psychology; Medicine; Clinical psychology; Psychiatry; Environmental health","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":[],"consensus_categories":[],"category_scores_codex":[0.0005624273,0.0001136516,0.0001746619,0.0003732812,0.00008991581,0.00004310665,0.00005975171,0.00006525074,0.00003169112],"category_scores_gemma":[0.00005421053,0.00008769202,0.0000706668,0.0003992727,0.00004902035,0.0001526856,0.00001871379,0.0003580125,5.522688e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001323051,"about_ca_system_score_gemma":0.00001231985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00134323,"about_ca_topic_score_gemma":0.001530436,"domain_scores_codex":[0.9988322,0.000318579,0.0002879433,0.0002313802,0.0001778742,0.0001520661],"domain_scores_gemma":[0.9996441,0.00009853083,0.00012554,0.00009232655,0.00002067463,0.00001882545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009086136,0.0003150649,0.9732219,0.0001929613,0.00005733454,0.000001504471,0.00341898,0.00156578,0.000006236245,0.00003169392,0.00002354158,0.02107413],"study_design_scores_gemma":[0.001249861,0.0004947549,0.9686669,0.003258643,0.0000631008,0.000003802443,0.001788739,0.02401991,0.000001776061,0.0003648275,8.719549e-7,0.0000868321],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954794,0.002685626,0.0005934021,0.00004028707,0.0001978621,0.0006998518,0.00001092215,0.00003196007,0.0002606928],"genre_scores_gemma":[0.999263,0.00008250723,0.0002495766,0.000002456318,0.00002995089,0.0001772846,0.0001613826,0.00001677321,0.00001706952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02245413,"threshold_uncertainty_score":0.3575977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0272953932279802,"score_gpt":0.3604342823790269,"score_spread":0.3331388891510467,"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."}}