{"id":"W4408346796","doi":"10.18632/oncotarget.28703","title":"Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients","year":2025,"lang":"en","type":"article","venue":"Oncotarget","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"","keywords":"Medicine; Precision medicine; Personalized medicine; Cancer Medicine; Oncology; Cancer; Internal medicine; Medical physics; Bioinformatics; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01680181,0.0009962531,0.0007188349,0.00189454,0.002266933,0.008762995,0.002240864,0.004439391,0.0529806],"category_scores_gemma":[0.01815465,0.0002921506,0.0008708411,0.001910612,0.001814595,0.006762896,0.009936489,0.006216742,0.02471431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00247782,"about_ca_system_score_gemma":0.01322652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002345069,"about_ca_topic_score_gemma":0.004717899,"domain_scores_codex":[0.9943738,0.002113648,0.0001771758,0.0006909177,0.002017632,0.0006267289],"domain_scores_gemma":[0.9814649,0.003121028,0.0008994997,0.001269496,0.003596606,0.009648554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001883143,0.00006911196,0.001850263,0.0002297427,0.00003408813,0.0001586676,0.0002070592,0.0002198746,0.000564915,0.01842526,0.749873,0.2281798],"study_design_scores_gemma":[0.0001290132,0.0001442207,0.001133308,0.0004971499,0.0000442718,0.0003743441,0.0003535979,0.0008336103,0.001175277,0.01732436,0.9779499,0.00004089376],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.009890274,0.04177259,0.03255624,0.6324201,0.03493046,0.0009248398,0.005717623,0.005269875,0.2365181],"genre_scores_gemma":[0.09684367,0.08565636,0.0897756,0.2615125,0.03005007,0.002173411,0.01629461,0.0044736,0.4132201],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.0529806,"threshold_uncertainty_score":0.1772377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02103837879809074,"score_gpt":0.3159929532191331,"score_spread":0.2949545744210424,"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."}}