{"id":"W4406896710","doi":"10.2196/58834","title":"A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study","year":2025,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Patient Satisfaction in Healthcare","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Patient experience; Medicine; Qualitative research; Scale (ratio); Health care; Psychology; Medical education; Nursing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001952303,0.0001551903,0.0003322878,0.0001794698,0.0006168656,0.00001075411,0.00009804704,0.00007919213,0.00007872072],"category_scores_gemma":[0.00005371086,0.0001267953,0.00001800066,0.0004867136,0.00003782226,0.0002223429,0.000129461,0.000401036,7.330439e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003948437,"about_ca_system_score_gemma":0.0004109601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006122055,"about_ca_topic_score_gemma":0.001693365,"domain_scores_codex":[0.9981159,0.0004896694,0.0004338984,0.0003675852,0.000305045,0.0002879054],"domain_scores_gemma":[0.9990448,0.0001137651,0.000235703,0.0001260365,0.0003923069,0.00008733762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002018272,0.00005444342,0.8619427,0.0002471445,0.00003806788,2.333496e-7,0.1273322,0.009129024,0.00003050382,0.00001663415,0.00001065211,0.0009964977],"study_design_scores_gemma":[0.001555762,0.0003068709,0.7274164,0.0007103135,0.0000385075,4.87426e-8,0.218334,0.05133587,0.00004157905,0.00001549437,0.00004491676,0.0002002923],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935003,0.0002631352,0.002520693,0.00004966021,0.0006153141,0.002835704,0.00003014475,0.00004113471,0.0001439246],"genre_scores_gemma":[0.9936737,0.00002541311,0.0003917801,0.00009768009,0.00004541584,0.00564641,0.000007681318,0.00001622058,0.00009564897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1345264,"threshold_uncertainty_score":0.9254757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1535814742528035,"score_gpt":0.5074529862846351,"score_spread":0.3538715120318316,"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."}}