{"id":"W2519597521","doi":"10.3999/jscpt.44.47","title":"Current Status and Future Expectations of Using Remote Source Data Verification for Improving the Efficiency of Clinical Trials","year":2013,"lang":"en","type":"article","venue":"Rinsho yakuri/Japanese Journal of Clinical Pharmacology and Therapeutics","topic":"Innovative Microfluidic and Catalytic Techniques Innovation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Association of Canadian Archivists","funders":"","keywords":"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1715378,0.0003744301,0.0005014878,0.001448227,0.0006593742,0.004872693,0.002733714,0.002872452,0.004125005],"category_scores_gemma":[0.2675064,0.0004879666,0.000877294,0.002055649,0.002081301,0.006317243,0.001984789,0.002202484,0.0006750221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003127009,"about_ca_system_score_gemma":0.01312489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00238496,"about_ca_topic_score_gemma":0.002976401,"domain_scores_codex":[0.9085362,0.06638873,0.009844419,0.002630455,0.009582971,0.003017192],"domain_scores_gemma":[0.4958282,0.2692749,0.1059569,0.01201901,0.06030763,0.0566134],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001890739,0.001347659,0.6241596,0.005499433,0.0002697332,0.0006422457,0.00503247,0.00212165,0.002590472,0.00300553,0.01400449,0.3394359],"study_design_scores_gemma":[0.0006306205,0.00490987,0.8926378,0.007533419,0.0003940373,0.002456323,0.01891007,0.01681457,0.003115764,0.004032233,0.04823431,0.000331048],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.556863,0.05763248,0.0241346,0.3392571,0.001044639,0.0006818726,0.0008181925,0.0005854824,0.0189826],"genre_scores_gemma":[0.9573423,0.01259022,0.01639449,0.01153365,0.0008726485,0.0003116249,0.0004088863,0.00004813327,0.0004981356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8284622,"threshold_uncertainty_score":0.9071892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2168482143777374,"score_gpt":0.4907498686909708,"score_spread":0.2739016543132334,"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."}}