{"id":"W3203935034","doi":"10.1080/10428194.2021.1948026","title":"Anticipation in multiple-case lymphoid cancer families after controlling for ascertainment biases","year":2021,"lang":"en","type":"letter","venue":"Leukemia & lymphoma/Leukemia and lymphoma","topic":"DNA Repair Mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Anticipation (artificial intelligence); Inheritance (genetic algorithm); Disease; Cancer; Medicine; Oncology; Biology; Genetics; Internal medicine; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005363797,0.0004114398,0.0007005291,0.0008505381,0.0009009668,0.000650041,0.0006716589,0.002063322,0.002101468],"category_scores_gemma":[0.02820193,0.0003200913,0.0003876884,0.001229963,0.0006012348,0.0007808923,0.000461032,0.001380752,0.0005416782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00046994,"about_ca_system_score_gemma":0.0003865995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003351035,"about_ca_topic_score_gemma":0.005875504,"domain_scores_codex":[0.9970436,0.001769474,0.0001484276,0.0006127473,0.0002196069,0.0002061287],"domain_scores_gemma":[0.9815801,0.01380151,0.002121789,0.001440498,0.0005394542,0.0005166491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002434099,0.00009805808,0.8497203,0.0001098004,0.0003607409,0.05396959,0.0005460978,0.001649834,0.003846314,0.003665432,0.02445917,0.05914051],"study_design_scores_gemma":[0.0003032022,0.000972718,0.6805335,0.0002529658,0.0009057217,0.2019743,0.0009385947,0.03771864,0.00329231,0.04536881,0.02753635,0.0002028642],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8982455,0.004195218,0.0268347,0.05652339,0.002505903,0.0001002008,0.001646667,0.0002896962,0.009658765],"genre_scores_gemma":[0.9853006,0.0008964156,0.003869813,0.004894251,0.002191641,0.00005972058,0.0002424973,0.00003552894,0.002509552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005363797,"threshold_uncertainty_score":0.0283668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01982522826442308,"score_gpt":0.2570912003199949,"score_spread":0.2372659720555718,"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."}}