{"id":"W4306411219","doi":"10.1002/ajh.26767","title":"Utilization of <scp>real‐world</scp> data in assessing treatment effectiveness for diffuse large <scp>B‐cell</scp> lymphoma","year":2022,"lang":"en","type":"review","venue":"American Journal of Hematology","topic":"Lymphoma Diagnosis and Treatment","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Spinal Cord Injury BC; University of British Columbia","funders":"MorphoSys; Verastem Oncology; Teva Pharmaceutical Industries; Genentech; Celgene; Adaptive Biotechnologies; Gilead Sciences; TG Therapeutics; Pfizer; Bristol-Myers Squibb","keywords":"Observational study; Medicine; Covariate; Clinical trial; Randomized controlled trial; Oncology; Propensity score matching; Diffuse large B-cell lymphoma; Sample size determination; Comparative effectiveness research; Clinical study design; Internal medicine; Lymphoma; Computer science; Statistics; Machine learning; Pathology; Alternative medicine","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":[0.01404616,0.0009370043,0.005293972,0.00613948,0.0002411167,0.002394038,0.001193926,0.001535169,0.003595557],"category_scores_gemma":[0.02682579,0.0004665903,0.004265066,0.005647339,0.0006445936,0.001830835,0.0007516429,0.002093239,0.0004885205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001785261,"about_ca_system_score_gemma":0.004174463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002352868,"about_ca_topic_score_gemma":0.00544875,"domain_scores_codex":[0.9936645,0.003331134,0.001190254,0.0004131299,0.001286342,0.0001147717],"domain_scores_gemma":[0.9630441,0.03127825,0.002868273,0.0006486276,0.001957918,0.0002028953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000357202,0.00006268359,0.0005827286,0.2650104,0.005831556,0.00005441092,0.00007823248,0.0006289494,0.0005228594,0.002893548,0.005332296,0.718645],"study_design_scores_gemma":[0.0009336168,0.001388676,0.01402172,0.4246616,0.04556434,0.001179288,0.0004053671,0.001306515,0.001376957,0.007278508,0.5017544,0.0001289774],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001882729,0.998688,0.0001931787,0.0002182202,0.00006874408,0.00005388503,0.0000866367,0.000004728107,0.0004982278],"genre_scores_gemma":[0.004265845,0.9940912,0.0009407435,0.0003520596,0.00007016771,0.0001033454,0.00008835272,0.000003650831,0.00008457024],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01404616,"threshold_uncertainty_score":0.07428408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08210967973667803,"score_gpt":0.396500964877894,"score_spread":0.3143912851412159,"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."}}