{"id":"W4291143774","doi":"10.1186/s12911-022-01965-9","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":"article","venue":"BMC Medical Informatics and Decision Making","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Commission for Science and Technology; International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Data science; Data sharing; Health informatics; Leverage (statistics); Computer science; Informatics; Analytics; Data mining; Medicine; Public health; Engineering; Nursing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.108758,0.001031129,0.0007323215,0.002392851,0.001637014,0.003258953,0.002531331,0.001660557,0.004888624],"category_scores_gemma":[0.06706517,0.001097194,0.001895744,0.002897631,0.003519597,0.002449743,0.005928329,0.002243116,0.0009990704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002833302,"about_ca_system_score_gemma":0.01701995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003024339,"about_ca_topic_score_gemma":0.003683199,"domain_scores_codex":[0.9168171,0.07086404,0.003078143,0.002705184,0.005169697,0.001365831],"domain_scores_gemma":[0.9371812,0.03533302,0.00505377,0.007409726,0.01215626,0.002866031],"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":[0.01007309,0.01385699,0.2053544,0.02495371,0.002892124,0.001505925,0.01371203,0.02597342,0.007613035,0.1360011,0.02379919,0.534265],"study_design_scores_gemma":[0.01918152,0.04736254,0.1863218,0.02971901,0.007896204,0.003205242,0.03487574,0.09701305,0.04232165,0.2040821,0.3269625,0.001058607],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.1808753,0.003946431,0.3393467,0.01162228,0.0006150462,0.431794,0.01185424,0.0005938362,0.01935196],"genre_scores_gemma":[0.1569096,0.001476271,0.5873004,0.002278908,0.0001920211,0.2486689,0.001774073,0.00008776288,0.00131205],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.108758,"threshold_uncertainty_score":0.5751744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.43642021554935,"score_gpt":0.4914372978798514,"score_spread":0.0550170823305014,"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."}}