{"id":"W2325333960","doi":"10.1017/cjn.2014.72","title":"Characterization of the chemoresistance profiles of malignant gliomas- A step towards a predictive individualized treatment","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Medicine; Oncology; Glioma; Glioblastoma; Internal medicine; Cancer research","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0009426285,0.0004237111,0.000987735,0.002283483,0.0002368549,0.001727165,0.0004574762,0.0005515103,0.001149695],"category_scores_gemma":[0.002130943,0.0002098141,0.0003630174,0.0009160597,0.0002557727,0.000938869,0.0003797438,0.0006467861,0.000529172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003682289,"about_ca_system_score_gemma":0.0003909149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009405844,"about_ca_topic_score_gemma":0.001079534,"domain_scores_codex":[0.999652,0.00009469262,0.00004298391,0.0000693836,0.00008745501,0.00005338232],"domain_scores_gemma":[0.9990855,0.0003514447,0.0002208341,0.0001083249,0.0001665972,0.00006721693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001054587,0.0002599971,0.5429791,0.0004790701,0.0002568362,0.000577866,0.0004115513,0.004556999,0.1184271,0.001443333,0.003447013,0.3261067],"study_design_scores_gemma":[0.00003254236,0.0006717863,0.8938238,0.0002260769,0.0004718101,0.004074248,0.0009612406,0.02938245,0.04349665,0.007195828,0.01956614,0.00009740087],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9431037,0.01954401,0.02680303,0.002045633,0.00005210066,0.0001290832,0.00255064,0.0004226378,0.005349212],"genre_scores_gemma":[0.9859283,0.003387833,0.007633025,0.0002133209,0.00008532839,0.00005159939,0.001872819,0.00005638152,0.0007713222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002283483,"threshold_uncertainty_score":0.004985154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01915336343262371,"score_gpt":0.2680762502042669,"score_spread":0.2489228867716432,"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."}}