{"id":"W7155613118","doi":"10.1145/3787279.3788493","title":"Predicting Enteric Fever Severity Using Non-Clinical Data: A Machine Learning Approach for Low-Resource Settings","year":2025,"lang":"","type":"article","venue":"","topic":"Viral gastroenteritis research and epidemiology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Royal University","funders":"","keywords":"Logistic regression; Public health; Socioeconomic status; Random forest; Predictive modelling; Support vector machine; Disease; Public health surveillance","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002860434,0.0006477383,0.000712806,0.002954226,0.0003538592,0.00157296,0.0005584073,0.0006753184,0.0005803085],"category_scores_gemma":[0.01047611,0.0001858375,0.0005478358,0.001791271,0.0002120204,0.0009147256,0.0006528645,0.0009104508,0.0002338141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007059288,"about_ca_system_score_gemma":0.0009125294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007736559,"about_ca_topic_score_gemma":0.01123494,"domain_scores_codex":[0.9986241,0.0007395323,0.0001955464,0.0002023843,0.0001478792,0.00009052514],"domain_scores_gemma":[0.9924793,0.005650919,0.0007810891,0.0003195986,0.0005721168,0.0001969264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003407949,0.0005471046,0.8482856,0.0002223357,0.0002429358,0.0003257731,0.0001927678,0.04151956,0.001152135,0.0003465141,0.001613837,0.1052106],"study_design_scores_gemma":[0.00004813184,0.0006762011,0.3229848,0.0002432184,0.000120748,0.000514217,0.001375863,0.6667702,0.00173058,0.003487902,0.001993888,0.00005417908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9673352,0.001314143,0.02367945,0.00178063,0.00006249481,0.0002186444,0.003962178,0.0001745901,0.001472629],"genre_scores_gemma":[0.9810146,0.0003035769,0.01635059,0.0001043743,0.00003180656,0.000056402,0.001972681,0.000003848335,0.0001621292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007736559,"threshold_uncertainty_score":0.01538306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08266117515474096,"score_gpt":0.4039818244348216,"score_spread":0.3213206492800806,"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."}}