{"id":"W4379185221","doi":"10.2196/45872","title":"Creating an Innovative Artificial Intelligence–Based Technology (TCRact) for Designing and Optimizing T Cell Receptors for Use in Cancer Immunotherapies: Protocol for an Observational Trial","year":2023,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"CAR-T cell therapy research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund","keywords":"T-cell receptor; Immunotherapy; Medicine; Observational study; Colorectal cancer; Cancer immunotherapy; Clinical trial; Oncology; Cancer; Internal medicine; Bioinformatics; Immune system; Immunology; T cell; Biology","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":[0.05181256,0.002413577,0.0023758,0.001499102,0.002807324,0.002047946,0.002341688,0.003286842,0.02361166],"category_scores_gemma":[0.04597016,0.001355322,0.002735205,0.00186652,0.002289724,0.001536938,0.003014012,0.00417634,0.006667361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003121337,"about_ca_system_score_gemma":0.01751181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001208216,"about_ca_topic_score_gemma":0.001532858,"domain_scores_codex":[0.9725823,0.01942112,0.00294575,0.001462761,0.002444413,0.001143696],"domain_scores_gemma":[0.9701099,0.007060341,0.004914106,0.007087694,0.007708021,0.003119902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.5909763,0.05949647,0.01282155,0.01357839,0.002353978,0.001118582,0.00142732,0.01466426,0.01615427,0.0195876,0.05648268,0.2113387],"study_design_scores_gemma":[0.5093434,0.1883446,0.02179261,0.004240772,0.001708879,0.0004774698,0.0005420644,0.006706253,0.009152354,0.0148038,0.2424759,0.0004120636],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.004788914,0.0001718693,0.008228548,0.0004259476,0.0003060419,0.9830732,0.001464476,0.00007430503,0.001466541],"genre_scores_gemma":[0.003287069,0.0001061933,0.008665146,0.0002629766,0.00005210576,0.9869811,0.0002963733,0.000006154974,0.0003428591],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.05181256,"threshold_uncertainty_score":0.2740143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6942350076305847,"score_gpt":0.624114053398265,"score_spread":0.07012095423231968,"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."}}