{"id":"W4406100364","doi":"10.2196/65001","title":"A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study","year":2025,"lang":"en","type":"article","venue":"JMIR Bioinformatics and Biotechnology","topic":"Cancer survivorship and care","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Term (time); Feature selection; Selection (genetic algorithm); Feature (linguistics); Artificial intelligence; Computer science; Machine learning; Psychology; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00367723,0.000480276,0.0003630501,0.0007668231,0.0002924019,0.0003320704,0.0005046807,0.0004227623,0.0005570236],"category_scores_gemma":[0.005216566,0.000234719,0.0006426041,0.0004341961,0.0001898258,0.0003889198,0.0004241715,0.000500256,0.0001287446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003475613,"about_ca_system_score_gemma":0.0005201864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007226911,"about_ca_topic_score_gemma":0.007775653,"domain_scores_codex":[0.9992365,0.0004444704,0.00004918481,0.0001361148,0.00007385007,0.00005995455],"domain_scores_gemma":[0.9976149,0.0009524954,0.0003237914,0.0002736914,0.0006698214,0.0001653021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005470369,0.0008463732,0.9692292,0.00003423437,0.0003291565,0.0001085626,0.0001636617,0.003871557,0.00113711,0.00006687875,0.0005432163,0.02312315],"study_design_scores_gemma":[0.0001327135,0.002721618,0.8475961,0.0000322484,0.000580045,0.0004671534,0.0005952056,0.1432815,0.003113949,0.0003083834,0.001127501,0.00004341761],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925743,0.00009025699,0.006768442,0.00005701205,0.000008754438,0.00006286044,0.000311276,0.00002540152,0.0001016456],"genre_scores_gemma":[0.9928771,0.00004322613,0.006285992,0.0000347812,0.000008080061,0.00008439908,0.0005013903,0.000004516107,0.0001606442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007226911,"threshold_uncertainty_score":0.01944733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01396898966182275,"score_gpt":0.3312783552135731,"score_spread":0.3173093655517504,"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."}}