{"id":"W4415484460","doi":"10.2196/68027","title":"Predicting Postoperative Recurrence Using a Support Vector Machine for Patients With Esophageal Squamous Cell Carcinoma: Machine Learning Modeling Development and Validation Study","year":2025,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Esophageal Cancer Research and Treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Discriminative model; Support vector machine; Esophageal squamous cell carcinoma; Relevance vector machine; Risk assessment","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":[0.0001653324,0.0002251417,0.0002971105,0.0001471846,0.0003571774,0.00004320061,0.0000550044,0.00004352495,0.00002799072],"category_scores_gemma":[0.00003898421,0.0001723354,0.00003153027,0.0002120174,0.00002533935,0.0001256033,0.00006618202,0.0002452154,7.23597e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005684427,"about_ca_system_score_gemma":0.0006315622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007614629,"about_ca_topic_score_gemma":0.0002580197,"domain_scores_codex":[0.998549,0.00005694787,0.0002712308,0.000450505,0.0003415943,0.00033069],"domain_scores_gemma":[0.9993001,0.00005561447,0.00008617329,0.000128013,0.0002960843,0.0001339858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001507706,0.0006453702,0.98862,0.0002734557,0.00007926151,0.00002959858,0.002709166,0.001421448,0.00003629038,0.000003321904,0.000007223247,0.004667119],"study_design_scores_gemma":[0.03519698,0.01075508,0.671565,0.0004311578,0.0005984581,0.00000940508,0.0009709051,0.2726891,0.007099885,0.00001050193,0.0001170892,0.0005564612],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935623,0.002009435,0.0009410852,0.00007034737,0.00007067795,0.003179565,0.000058871,0.00003916411,0.00006853081],"genre_scores_gemma":[0.9970192,0.000007969621,0.001382844,0.00005261314,0.00004072713,0.0009262516,0.0001702889,0.00002826377,0.0003718466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3170551,"threshold_uncertainty_score":0.7027634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02675667800598665,"score_gpt":0.3316313571248495,"score_spread":0.3048746791188628,"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."}}