{"id":"W4213266894","doi":"10.1093/jcag/gwab049.230","title":"A231 COLORECTAL CANCER PROVINCIAL SCREENING OPTIMIZATION USING GUT MICROBIOME AS BIOMARKER","year":2022,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Medicine; Colonoscopy; Colorectal cancer; Fecal occult blood; Microbiome; Internal medicine; False positive paradox; Gastroenterology; Adenoma; Feces; Biomarker; Stage (stratigraphy); Colorectal cancer screening; Oncology; Cancer; Bioinformatics; Biology; Artificial intelligence; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004906939,0.0007866563,0.001236779,0.001388321,0.0004358495,0.001125675,0.0003409607,0.0006165571,0.002821927],"category_scores_gemma":[0.001201083,0.0003220203,0.0008675366,0.0009422217,0.0001644032,0.0004727179,0.0006203904,0.0003478692,0.0008876378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004079698,"about_ca_system_score_gemma":0.0008113111,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003005557,"about_ca_topic_score_gemma":0.002644256,"domain_scores_codex":[0.9994882,0.0001082408,0.00002495841,0.0001401672,0.000161754,0.00007666016],"domain_scores_gemma":[0.9996512,0.00007915224,0.00006487451,0.00001979733,0.0001226882,0.00006226746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003983273,0.001380199,0.2086468,0.002298606,0.0006336645,0.001413154,0.0002240945,0.01730546,0.3923815,0.0006331442,0.006589597,0.3645104],"study_design_scores_gemma":[0.0004639439,0.01014766,0.3838377,0.0006265974,0.002791397,0.003831113,0.001109847,0.2208572,0.3324933,0.002580877,0.04088578,0.0003746041],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9329612,0.01794264,0.03691825,0.001064908,0.0002778611,0.0003941023,0.002569106,0.0008915941,0.006980349],"genre_scores_gemma":[0.9613535,0.003491094,0.02930873,0.0003216375,0.00007898259,0.0002524591,0.001759455,0.00006027802,0.003373869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9969944,"threshold_uncertainty_score":0.009440243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0141852088685998,"score_gpt":0.2551861024768977,"score_spread":0.2410008936082979,"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."}}