{"id":"W3108105403","doi":"","title":"Leveraging a cloud-based critical care registry for pandemic surveillance and research in low and middle-income countries (Preprint)","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Preprint; Pandemic; Low and middle income countries; Cloud computing; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Business; Environmental health; Internet privacy; Computer security; Computer science; Medicine; Economic growth; Developing country; World Wide Web; Virology; Economics; Outbreak","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0116041,0.0007219439,0.0008407211,0.002419142,0.001417543,0.00489474,0.001877694,0.001096314,0.008096238],"category_scores_gemma":[0.04222416,0.0007412442,0.0009718733,0.00382278,0.0006249821,0.00590617,0.005653914,0.001528846,0.003631964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00116693,"about_ca_system_score_gemma":0.005572815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01474734,"about_ca_topic_score_gemma":0.01252764,"domain_scores_codex":[0.9921744,0.003622143,0.0009527957,0.001273767,0.001184564,0.000792336],"domain_scores_gemma":[0.9651891,0.008870207,0.002818453,0.01280961,0.006260986,0.004051665],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003166849,0.001241641,0.2566761,0.0008940774,0.0007523776,0.001270136,0.002174129,0.03617557,0.009538319,0.02616095,0.2683146,0.3936352],"study_design_scores_gemma":[0.001467834,0.001464685,0.1356891,0.001041543,0.0007558506,0.001384476,0.009863102,0.5121002,0.01986661,0.06418707,0.251663,0.0005166185],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3273467,0.002504033,0.4506426,0.05097138,0.007446477,0.004542636,0.058579,0.05731025,0.04065688],"genre_scores_gemma":[0.7235395,0.0009758141,0.2464581,0.002085934,0.001070524,0.0006391424,0.02061095,0.00101013,0.003609869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9883959,"threshold_uncertainty_score":0.06136906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.36136414645896,"score_gpt":0.4794681190858623,"score_spread":0.1181039726269023,"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."}}