{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003113057,0.0001365161,0.0001864647,0.0002510072,0.0005495085,0.0006037834,0.0002457612,0.000388586,0.001990648],"category_scores_gemma":[0.002695816,0.00005641535,0.0003097641,0.0002065731,0.0001600742,0.0004361733,0.000521141,0.0007478444,0.0001811009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000357931,"about_ca_system_score_gemma":0.001115205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001124225,"about_ca_topic_score_gemma":0.00317268,"domain_scores_codex":[0.9997961,0.00006523229,0.00001790902,0.00002120494,0.00004558215,0.0000541162],"domain_scores_gemma":[0.999653,0.0001003095,0.0001039557,0.000009287903,0.00004755717,0.00008600723],"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":[0.001097874,0.0009286888,0.4855754,0.0006625868,0.0002033678,0.002089614,0.001982032,0.001169904,0.003541659,0.00251701,0.00777072,0.4924611],"study_design_scores_gemma":[0.000163743,0.003036017,0.8983712,0.002833633,0.0007548233,0.008482738,0.01575217,0.003863274,0.005059852,0.01479812,0.04671945,0.0001650566],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9378495,0.02515669,0.003695261,0.01692079,0.0005072983,0.0001112573,0.000321755,0.00004674934,0.01539071],"genre_scores_gemma":[0.9877457,0.007198533,0.001952692,0.001136224,0.0001766232,0.00006888647,0.0001888735,0.000009916412,0.001522449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001990648,"threshold_uncertainty_score":0.006659389,"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."}}