{"id":"W4220782125","doi":"10.21203/rs.3.rs-1418826/v1","title":"Leveraging Artificial Intelligence and Data Science Techniques in Harmonizing, Sharing, Accessing and Analyzing SARS-COV-2/COVID-19 Data in Rwanda (LAISDAR Project): Study design and rationale","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sudbury Regional Hospital","funders":"International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Data science; Leverage (statistics); Data sharing; Computer science; Informatics; Analytics; Data mining; Medicine; Engineering; Artificial intelligence","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":[0.113679,0.0009505271,0.0007322884,0.002342921,0.001574204,0.003251412,0.002341111,0.001500753,0.00496933],"category_scores_gemma":[0.07592202,0.001034401,0.001896244,0.002889011,0.003077597,0.002318669,0.005434482,0.002046483,0.0009943387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002681953,"about_ca_system_score_gemma":0.01591524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0028583,"about_ca_topic_score_gemma":0.003373624,"domain_scores_codex":[0.9117379,0.07606164,0.003267527,0.002535262,0.005205806,0.001191872],"domain_scores_gemma":[0.9271103,0.04148683,0.005617462,0.007965481,0.01506057,0.002759415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01021822,0.0132859,0.2103587,0.02841505,0.003441551,0.001466472,0.01434084,0.02421334,0.00667488,0.1337545,0.02583222,0.5279983],"study_design_scores_gemma":[0.01689868,0.04711221,0.2112743,0.04142795,0.008845554,0.002910025,0.04076025,0.09661017,0.03815472,0.1724475,0.3224861,0.001072649],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.2069201,0.0046188,0.311104,0.01225691,0.000634805,0.4294952,0.01344173,0.0005349761,0.02099353],"genre_scores_gemma":[0.1882524,0.00164295,0.528473,0.002256939,0.0001988706,0.275767,0.001934654,0.00008710213,0.001387032],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.113679,"threshold_uncertainty_score":0.6011992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6301397998653303,"score_gpt":0.5628358124767353,"score_spread":0.067303987388595,"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."}}