{"id":"W4413539704","doi":"10.64628/aam.73wewhtus","title":"All for one, not one for all: The promise and challenges of personalized medicine","year":2024,"lang":"en","type":"article","venue":"","topic":"CAR-T cell therapy research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Personalized medicine; Precision medicine; Medicine; Internet privacy; Data science; Computer science; Bioinformatics; Biology; Pathology","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":[0.01772386,0.0007309637,0.00246812,0.001233202,0.003444938,0.009306363,0.001623046,0.007069109,0.01040913],"category_scores_gemma":[0.02296628,0.0005119204,0.001266239,0.001008858,0.01299806,0.0193525,0.005207819,0.01998988,0.00360969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003327851,"about_ca_system_score_gemma":0.007042689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001621933,"about_ca_topic_score_gemma":0.004143639,"domain_scores_codex":[0.9916158,0.004493642,0.000341764,0.0009757185,0.001910113,0.0006629392],"domain_scores_gemma":[0.9706727,0.02153268,0.001206292,0.001865081,0.001961225,0.002762017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004413074,0.0003566005,0.00443459,0.002025567,0.0003331633,0.0005708865,0.002067297,0.0009354393,0.001750461,0.280783,0.2295097,0.4767919],"study_design_scores_gemma":[0.0001076307,0.0003093495,0.002076624,0.001993296,0.0002431544,0.001380187,0.003152324,0.001246489,0.001023291,0.5216283,0.4666816,0.0001576983],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.004138346,0.1640768,0.009315081,0.7997217,0.007645806,0.00002813371,0.0002098459,0.0001598627,0.0147045],"genre_scores_gemma":[0.2065458,0.2965883,0.02166623,0.4300194,0.03233914,0.0001946869,0.000369236,0.0003014677,0.01197573],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01772386,"threshold_uncertainty_score":0.09373385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2042038018108626,"score_gpt":0.3993359464152614,"score_spread":0.1951321446043988,"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."}}