{"id":"W4394573405","doi":"10.1101/2024.04.04.24305299","title":"CLINICAL SAMPLING OF SMALL INTESTINE LUMINAL CONTENT FOR MICROBIOME MULTI-OMICS ANALYSIS: A PERFORMANCE ANALYSIS OF THE SMALL INTESTINE MICROBIOME ASPIRATION (SIMBA) CAPSULE AND BENCHMARKING AGAINST ENDOSCOPY","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"International Microbiome Centre, University of Calgary; Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Capsule endoscopy; Gastroenterology; Medicine; Microbiome; Small intestine; Internal medicine; Sampling (signal processing); Context (archaeology); Omics; Irritable bowel syndrome; Biology; Bioinformatics; Computer science","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":[0.005060858,0.0009666023,0.001075204,0.001159174,0.000599817,0.002170813,0.0005920809,0.0009138162,0.00122143],"category_scores_gemma":[0.007823206,0.0004161423,0.0008074397,0.001061582,0.0007179959,0.0005100833,0.001748601,0.0006216117,0.0008343363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003053804,"about_ca_system_score_gemma":0.0007572706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00144901,"about_ca_topic_score_gemma":0.001893967,"domain_scores_codex":[0.9960172,0.001621371,0.0002680581,0.0007882419,0.001126011,0.000179208],"domain_scores_gemma":[0.9963528,0.001033351,0.0007950782,0.0005319389,0.00101832,0.0002684268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005459054,0.0007801343,0.5437039,0.001344485,0.001064638,0.0008350302,0.0009683952,0.005343383,0.3157727,0.0005529707,0.00365969,0.1205157],"study_design_scores_gemma":[0.0002406719,0.006792306,0.7232652,0.0004066401,0.001097009,0.004236708,0.001671631,0.06798118,0.1791284,0.0009013331,0.01403418,0.0002447353],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9026663,0.00394426,0.08558988,0.0004730217,0.000161987,0.0007346947,0.00336518,0.0007902423,0.002274474],"genre_scores_gemma":[0.916127,0.0009853679,0.07708894,0.0004269216,0.0001089738,0.0005026986,0.003700342,0.0001654903,0.0008942112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005060858,"threshold_uncertainty_score":0.02676469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09454386755235976,"score_gpt":0.3355699432148571,"score_spread":0.2410260756624973,"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."}}