{"id":"W4220738779","doi":"10.53773/ijcom.v1i3.39.125-8","title":"Utilization of Predictive Models for Diagnosis of Occupational Diseases","year":2022,"lang":"en","type":"article","venue":"The Indonesian Journal of Community and Occupational Medicine","topic":"Occupational exposure and asthma","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Occupational asthma; Medicine; Asthma; Predictive modelling; Disease; Diagnostic test; Occupational lung disease; Developing country; Test (biology); Environmental health; Family medicine; Pediatrics; Pathology; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001166729,0.0001126471,0.0004274725,0.0002697301,0.0003685534,0.000002817888,0.0002103714,0.00003279352,0.0001077301],"category_scores_gemma":[0.0005151909,0.00007646315,0.0001254536,0.0002932697,0.0003615379,0.0001544022,0.00007180866,0.0003288968,5.87536e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004079822,"about_ca_system_score_gemma":0.0003789808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004712779,"about_ca_topic_score_gemma":0.000004251084,"domain_scores_codex":[0.997931,0.0003948634,0.0007603837,0.00005546062,0.0007662356,0.00009202724],"domain_scores_gemma":[0.996116,0.001837837,0.0008348702,0.0001881187,0.0009333559,0.00008986445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.03418454,0.002341181,0.889195,0.0005912342,0.0007872006,0.00000621846,0.009308082,0.01364885,0.000363301,0.02536464,0.006019504,0.01819026],"study_design_scores_gemma":[0.004358765,0.004805535,0.9688705,0.0002787884,0.0004816866,0.000151808,0.004731016,0.002811355,0.0004895602,0.01241642,0.0005258447,0.00007878376],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788646,0.001459251,0.01611006,0.002507618,0.0001436801,0.0004015929,0.0003839784,0.000004809975,0.0001244863],"genre_scores_gemma":[0.9986565,0.0001175515,0.0003617879,0.000380635,0.0001990564,0.00003969384,0.0002191537,0.00001019465,0.00001544443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07967546,"threshold_uncertainty_score":0.3118077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.106718001916086,"score_gpt":0.3555414558353371,"score_spread":0.2488234539192511,"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."}}