{"id":"W1903591517","doi":"10.5210/ojphi.v7i1.5849","title":"Integrated Disease Surveillance to Reduce Data Fragmentation – An Application to Malaria Control","year":2015,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"McGill University; Bill and Melinda Gates Foundation","keywords":"Malaria; Cornerstone; Data sharing; Computer science; Data integration; Fragmentation (computing); Disease surveillance; Data science; Infectious disease (medical specialty); Psychological intervention; Automatic identification and data capture; Disease control; Process management; Risk analysis (engineering); Medicine; Disease; Data mining; Environmental health; Geography; Business","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":[0.01033513,0.0007685221,0.0008617004,0.004417294,0.001040589,0.002752285,0.002017442,0.0008533205,0.00236528],"category_scores_gemma":[0.02060614,0.0005629939,0.001062352,0.005329687,0.0009038235,0.00381382,0.005966553,0.00107756,0.000498149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009659906,"about_ca_system_score_gemma":0.002461218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008026662,"about_ca_topic_score_gemma":0.005679349,"domain_scores_codex":[0.9956722,0.00184604,0.0005222523,0.0007995324,0.0009809168,0.0001790397],"domain_scores_gemma":[0.9869642,0.005461087,0.001235739,0.003764756,0.001783979,0.000790239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005994929,0.0007460638,0.03697405,0.0007084017,0.0005340494,0.0004277913,0.003518281,0.02527263,0.01245353,0.0171179,0.01743024,0.8842176],"study_design_scores_gemma":[0.0005294225,0.0007577727,0.04547236,0.0006962044,0.0005696003,0.0009722491,0.00392073,0.666747,0.03544049,0.1038949,0.1407659,0.0002333237],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07782328,0.001590863,0.8792629,0.005988576,0.0002353063,0.001296984,0.003865398,0.02378456,0.006152089],"genre_scores_gemma":[0.2759077,0.000723877,0.7162673,0.0004668658,0.00008267679,0.000355493,0.004600709,0.0003556267,0.001239739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01033513,"threshold_uncertainty_score":0.05465806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.118572227708889,"score_gpt":0.4103008139780077,"score_spread":0.2917285862691186,"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."}}