{"id":"W7131857651","doi":"10.5376/cge.2025.13.0009","title":"Intraoperative Risk Management and Postoperative Recovery Strategies for Cervical Cancer Patients","year":2025,"lang":"","type":"article","venue":"Cancer Genetics and Epigenetics","topic":"Enhanced Recovery After Surgery","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Cervical cancer; MEDLINE; Disease; Cancer; Risk assessment; Complication","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000245534,0.0007161956,0.0009344858,0.0002760526,0.0005172542,0.0003285201,0.0001842891,0.0004060719,0.00012083],"category_scores_gemma":[0.00005279232,0.0006914003,0.0001591058,0.000389854,0.0004884421,0.0001338539,0.0003530889,0.0004407589,0.000002577704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003233597,"about_ca_system_score_gemma":0.0007561218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002262414,"about_ca_topic_score_gemma":0.0007984778,"domain_scores_codex":[0.9965651,0.0001668548,0.0009148483,0.001205023,0.0003085716,0.0008396134],"domain_scores_gemma":[0.9978947,0.0003058024,0.0002820615,0.0004589494,0.0006999293,0.0003585537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001780223,0.0005360345,0.3414819,0.003051388,0.003455967,0.00001405699,0.003598417,0.004588897,0.0004662313,0.001190075,0.00501667,0.6348202],"study_design_scores_gemma":[0.01186002,0.00444854,0.8119872,0.004861385,0.00654092,0.000002986643,0.005477092,0.01416524,0.02425237,0.01235948,0.1014986,0.002546198],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8096441,0.171052,0.00957708,0.0007227011,0.003596975,0.002522039,0.001472459,0.00002337575,0.001389225],"genre_scores_gemma":[0.554193,0.4381321,0.002630068,0.001432439,0.0002957507,0.0006447425,0.0000442297,0.00006099368,0.002566732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.632274,"threshold_uncertainty_score":0.9995537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01160797488373895,"score_gpt":0.2859511791863648,"score_spread":0.2743432043026258,"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."}}