{"id":"W4367596807","doi":"10.54846/jshap/1315","title":"A sounder of swine: The importance of clustering in the design, analysis, and interpretation of clinical trials","year":2023,"lang":"en","type":"article","venue":"Journal of Swine Health and Production","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"University of Guelph","keywords":"Cluster analysis; Statistics; Sample size determination; Confidence interval; Sample (material); Interpretation (philosophy); Clinical trial; Econometrics; Computer science; Mathematics; Medicine; Internal medicine","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.7020086,0.002057084,0.009236747,0.0082522,0.006522679,0.01744611,0.006206575,0.01589358,0.00235673],"category_scores_gemma":[0.7572202,0.002847859,0.003635054,0.006549961,0.03211705,0.01500553,0.007094628,0.03197831,0.001272784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01253022,"about_ca_system_score_gemma":0.02376162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007178523,"about_ca_topic_score_gemma":0.00961876,"domain_scores_codex":[0.2036161,0.695778,0.03997794,0.01351818,0.04595067,0.001159073],"domain_scores_gemma":[0.10589,0.7373251,0.03931025,0.05345941,0.05771888,0.00629647],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00861665,0.000701394,0.02323098,0.016151,0.008578945,0.001054335,0.02499359,0.008627421,0.004630439,0.1089346,0.1586601,0.6358206],"study_design_scores_gemma":[0.003710791,0.008040493,0.03973678,0.03914246,0.004546229,0.002715995,0.01071462,0.04295603,0.005842024,0.568521,0.2718076,0.002265921],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009561373,0.06169306,0.504656,0.3731467,0.03901915,0.004167737,0.0003237632,0.0008443617,0.006587938],"genre_scores_gemma":[0.1799897,0.02109293,0.5744428,0.1863616,0.02814866,0.006986478,0.0001818846,0.00084102,0.001954991],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2979914,"threshold_uncertainty_score":0.3674765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3695120793276821,"score_gpt":0.5338763026583545,"score_spread":0.1643642233306724,"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."}}