{"id":"W4387115048","doi":"10.1001/jamaoncol.2023.3867","title":"Anti-TIGIT Antibody Tiragolumab Alone or With Atezolizumab in Patients With Advanced Solid Tumors","year":2023,"lang":"en","type":"article","venue":"JAMA Oncology","topic":"Cancer Immunotherapy and Biomarkers","field":"Medicine","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Roche (Canada); Princess Margaret Cancer Centre","funders":"Daiichi Sankyo Europe; National Center for Advancing Translational Sciences; National Cancer Institute; EMD Serono; Genentech; European Society for Medical Oncology; Servier; Yonsei University; MacroGenics; LG Chem; Regeneron Pharmaceuticals; BeiGene; F. Hoffmann-La Roche; Handok; Gilead Sciences; Yuhan; Eli Lilly and Company; AstraZeneca; Celgene; TG Therapeutics; Pfizer","keywords":"Medicine; Atezolizumab; Internal medicine; TIGIT; Tolerability; Oncology; Clinical endpoint; Clinical trial; Phases of clinical research; Antibody; Adverse effect; Immunology; Pembrolizumab; Immunotherapy; Cancer","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001934273,0.0002220922,0.0006918777,0.0002947637,0.00006574146,0.00001703487,0.0001239281,0.0001663335,0.0002112941],"category_scores_gemma":[0.0000528228,0.0001421992,0.00005243541,0.001045188,0.0002018372,0.0001210527,0.00004334304,0.0002660593,0.00008092604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003539961,"about_ca_system_score_gemma":0.0005552648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001422928,"about_ca_topic_score_gemma":0.0003615476,"domain_scores_codex":[0.9983132,0.0000682376,0.0003937807,0.0003984771,0.0003047444,0.0005215986],"domain_scores_gemma":[0.9991677,0.0001006027,0.0001808839,0.0003258088,0.0001219534,0.0001030779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.1209918,0.001250676,0.6189919,0.0001973935,0.0004206192,0.002914705,0.001023761,0.00008438841,0.005397582,0.00001701224,0.005413306,0.2432968],"study_design_scores_gemma":[0.03736293,0.01044863,0.9283243,0.0003951899,0.0000468759,0.0001140304,0.0003038547,0.00006495638,0.0009197655,0.000007403431,0.02176947,0.0002425385],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948077,0.00007203139,0.000009523125,0.00196692,0.0001638647,0.0007754728,0.0000194041,0.0001431976,0.002041941],"genre_scores_gemma":[0.9966364,0.0001808713,0.0003734508,0.001326879,0.00007712127,0.00006114857,0.0002185538,0.00005058708,0.001075018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3093324,"threshold_uncertainty_score":0.5798714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124915111144519,"score_gpt":0.3158602043763073,"score_spread":0.3033686932618554,"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."}}