{"id":"W2565248748","doi":"10.1158/1557-3265.ovcasymp14-as01","title":"Abstract AS01: Molecular genetic approaches to understanding ovarian cancer","year":2015,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University; McGill University and Génome Québec Innovation Centre; Jewish General Hospital","funders":"","keywords":"Sanger sequencing; COLD-PCR; Ovarian cancer; Biology; Cancer research; Exome sequencing; Mutation; Carboplatin; Metastasis; Cancer; Chemotherapy; Genetics; Gene; Point mutation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006187983,0.0003295772,0.0002613477,0.0008931937,0.0002641688,0.0009333823,0.0005766189,0.0008160757,0.008183206],"category_scores_gemma":[0.0006719012,0.0001052407,0.0002166351,0.0006304435,0.0006442039,0.001359275,0.0005318305,0.001179965,0.001759826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005800823,"about_ca_system_score_gemma":0.0003283087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006177224,"about_ca_topic_score_gemma":0.0005849266,"domain_scores_codex":[0.9998912,0.00004511506,0.000007917671,0.0000186851,0.0000237188,0.00001347315],"domain_scores_gemma":[0.9997228,0.00009015261,0.00003886193,0.00002283482,0.00006883638,0.0000565013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00038958,0.0002086455,0.01230101,0.001248606,0.00007463752,0.002760737,0.0005628621,0.005572262,0.102251,0.3981261,0.140628,0.3358766],"study_design_scores_gemma":[0.00007766685,0.0003028489,0.01583498,0.0003785846,0.00006455852,0.004808699,0.0006648696,0.01386396,0.01979754,0.2489243,0.6952332,0.00004889883],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.194332,0.1022007,0.3381469,0.1124596,0.006499554,0.0003886948,0.006140463,0.002792115,0.23704],"genre_scores_gemma":[0.6291965,0.07124858,0.1695565,0.01257494,0.004538172,0.0003615599,0.004582708,0.0004740213,0.1074669],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.008183206,"threshold_uncertainty_score":0.02737558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7612715649881563,"score_gpt":0.5303225873727327,"score_spread":0.2309489776154235,"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."}}