{"id":"W2512074876","doi":"10.1503/cmaj.1040806","title":"From bench to bedside and back again?","year":2004,"lang":"en","type":"editorial","venue":"Canadian Medical Association Journal","topic":"Lung Cancer Treatments and Mutations","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bench to bedside; Computer science; Data science; Medicine; World Wide Web; Medical physics","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":[0.01248958,0.004191988,0.004319708,0.002788319,0.003374397,0.008445503,0.003749822,0.01663894,0.01079388],"category_scores_gemma":[0.03971735,0.001504555,0.002261876,0.001629226,0.003744246,0.007629199,0.001435527,0.02997422,0.0117205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002664313,"about_ca_system_score_gemma":0.003246048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002213743,"about_ca_topic_score_gemma":0.006279583,"domain_scores_codex":[0.9939423,0.001741693,0.0007085147,0.000575567,0.002732401,0.0002994489],"domain_scores_gemma":[0.9666553,0.01666888,0.001504692,0.001008506,0.01017826,0.003984222],"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":[0.00006167351,0.00001596121,0.00003468937,0.0001867986,0.00001844844,0.00008944105,0.00002062017,0.00001809634,0.00005030589,0.0003753365,0.9929536,0.006175024],"study_design_scores_gemma":[0.0001110589,0.00007984066,0.0002745205,0.0004538658,0.00006274366,0.0002955912,0.0000971828,0.0001268267,0.0001283273,0.001444201,0.9968965,0.00002933937],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00004062619,0.005226949,0.0001359936,0.05341531,0.9398878,0.00001495703,0.00003329047,0.00007007135,0.001175059],"genre_scores_gemma":[0.0004590532,0.00434305,0.0001809064,0.04793988,0.9404513,0.00002043621,0.00002046964,0.00003438025,0.006550566],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.01663894,"threshold_uncertainty_score":0.06605202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004428764391674849,"score_gpt":0.283749636377891,"score_spread":0.2793208719862161,"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."}}