{"id":"W4390604494","doi":"10.1073/pnas.2315463120","title":"Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration","year":2024,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Parasites and Host Interactions","field":"Immunology and Microbiology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Allergy and Infectious Diseases; National Institutes of Health; School of Medicine, University of California, San Francisco; University Health Network","keywords":"Schistosomiasis; Mass drug administration; Schistosoma haematobium; Schistosoma; Hotspot (geology); Neglected tropical diseases; Tropical disease; Medicine; Environmental health; Schistosoma mansoni; Demography; Disease; Immunology; Internal medicine; Population; Helminths","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":[0.003928438,0.001270632,0.001167519,0.001539078,0.0004097428,0.00129088,0.001263335,0.0008933925,0.002684287],"category_scores_gemma":[0.007940657,0.000542414,0.001495386,0.000925481,0.0002406211,0.001134347,0.000873121,0.001801912,0.0008792597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009245767,"about_ca_system_score_gemma":0.002439033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02421783,"about_ca_topic_score_gemma":0.01973437,"domain_scores_codex":[0.9991259,0.0004309553,0.00005635152,0.000193558,0.00009866007,0.00009469515],"domain_scores_gemma":[0.996285,0.002527987,0.0003132861,0.0001191779,0.0006252811,0.0001292134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002679148,0.0002948708,0.06138219,0.0001593018,0.0003828334,0.0001359046,0.00008588687,0.808316,0.0009689786,0.003146676,0.008230873,0.1166286],"study_design_scores_gemma":[0.00002879892,0.00006631973,0.002513153,0.00002604723,0.00004435273,0.00001545106,0.00003163465,0.9942849,0.0002308021,0.002077884,0.0006692188,0.0000113623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2077238,0.002211114,0.7699047,0.004462687,0.000338702,0.0007039166,0.005544735,0.003326375,0.005783913],"genre_scores_gemma":[0.7885365,0.001336772,0.1982596,0.0008153105,0.0001965112,0.0007581161,0.006267535,0.0001568389,0.003672757],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02421783,"threshold_uncertainty_score":0.0481537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06398478542906307,"score_gpt":0.3702496085831311,"score_spread":0.3062648231540681,"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."}}