{"id":"W4402471159","doi":"10.1182/bloodadvances.2024012982","title":"Point-of-care CAR T manufacturing solutions: can 1 model fit all?","year":2024,"lang":"en","type":"article","venue":"Blood Advances","topic":"CAR-T cell therapy research","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Point (geometry); Computer science; Mathematics","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":[0.01182418,0.001365103,0.0008330998,0.001130805,0.001877038,0.01051074,0.003636572,0.008210831,0.02714007],"category_scores_gemma":[0.01806758,0.000508273,0.001333773,0.0009125998,0.003150344,0.01299245,0.004821837,0.008354289,0.01571345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003641154,"about_ca_system_score_gemma":0.008960374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001868401,"about_ca_topic_score_gemma":0.003493959,"domain_scores_codex":[0.9924918,0.002641753,0.0003698249,0.0005564486,0.003060272,0.0008798479],"domain_scores_gemma":[0.9889895,0.002301234,0.0009447479,0.001176334,0.003563698,0.003024578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004102926,0.0006402239,0.002440092,0.001340753,0.00009485706,0.0007214064,0.0007046093,0.003278742,0.002347639,0.3134487,0.4131655,0.2614073],"study_design_scores_gemma":[0.000117479,0.0008502664,0.0004473664,0.00104281,0.00007140251,0.0008876289,0.0009162102,0.002379334,0.001692573,0.08794051,0.9035547,0.00009978466],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.007411784,0.0395965,0.0902532,0.7302765,0.01310898,0.0004852286,0.000605693,0.002722806,0.1155393],"genre_scores_gemma":[0.2305179,0.1111524,0.2759197,0.2555307,0.01235167,0.001615915,0.002017376,0.00192263,0.1089716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02714007,"threshold_uncertainty_score":0.0907926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04513051343112905,"score_gpt":0.3392272802269209,"score_spread":0.2940967667957919,"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."}}