{"id":"W2118067068","doi":"10.1016/j.beha.2005.02.003","title":"Gene expression profiling and multiple myeloma","year":2005,"lang":"en","type":"review","venue":"Best Practice & Research Clinical Haematology","topic":"Multiple Myeloma Research and Treatments","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Gene expression profiling; Multiple myeloma; Computational biology; Biology; Gene expression; Gene; Profiling (computer programming); Bioinformatics; Cancer research; Genetics; Immunology; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.008540024,0.0007424039,0.00461578,0.0008491223,0.000514188,0.0001538532,0.0005959496,0.00182657,0.0002782588],"category_scores_gemma":[0.07420401,0.0005319723,0.0007514653,0.0007586073,0.001409289,0.0004505633,0.001401426,0.006020536,0.00306325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002861225,"about_ca_system_score_gemma":0.002557251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009117567,"about_ca_topic_score_gemma":0.00002313022,"domain_scores_codex":[0.9846785,0.007267118,0.002420307,0.001981785,0.001811578,0.001840698],"domain_scores_gemma":[0.9611779,0.0335069,0.0007371778,0.001688562,0.001157821,0.001731575],"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":[0.00106973,0.003318241,0.001003875,0.008863492,0.000726528,0.0104484,0.00002115304,3.286318e-8,0.000006824464,0.0001185543,0.001455806,0.9729674],"study_design_scores_gemma":[0.003126265,0.002103422,0.00002745948,0.008209116,0.001060922,0.01271923,0.0001179993,0.000057282,0.00004890262,0.00002839874,0.9721211,0.0003799646],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0007447351,0.9894028,0.00002585962,0.001238294,0.000238768,0.004902252,0.00006494213,0.000104698,0.003277652],"genre_scores_gemma":[0.0002896868,0.9706606,0.02524836,0.0001093416,0.001087642,0.0006298429,0.0003737186,0.0001738084,0.001427043],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9725874,"threshold_uncertainty_score":0.9997132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3913591458890733,"score_gpt":0.5953547236227015,"score_spread":0.2039955777336282,"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."}}