{"id":"W2791278436","doi":"10.1097/der.0000000000000342","title":"Rubber Accelerators in Medical Examination and Surgical Gloves","year":2018,"lang":"en","type":"article","venue":"Dermatitis","topic":"Contact Dermatitis and Allergies","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Surgical Gloves; Medicine; Product (mathematics); Product line; Medical emergency; Surgery; Medical physics; Manufacturing engineering; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00224664,0.000280771,0.000226848,0.001289893,0.0004744695,0.0008539676,0.0003988631,0.0009099572,0.01194784],"category_scores_gemma":[0.005565051,0.0002177399,0.0004026752,0.001618024,0.0004820844,0.001009457,0.0008997815,0.0004541949,0.001378451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000495011,"about_ca_system_score_gemma":0.0007116965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001458005,"about_ca_topic_score_gemma":0.002143737,"domain_scores_codex":[0.9969703,0.001027124,0.0005930257,0.0002768738,0.0009159393,0.0002166402],"domain_scores_gemma":[0.9897913,0.004051803,0.005036874,0.0002629519,0.0005829404,0.0002742551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001159046,0.0003530372,0.6100004,0.007811366,0.0001228952,0.004035018,0.005357117,0.0003083765,0.0157984,0.001810359,0.006165866,0.3470781],"study_design_scores_gemma":[0.00004121147,0.001594069,0.8289558,0.006024188,0.0001829869,0.03134352,0.007884037,0.0002514817,0.008616545,0.000651124,0.1143991,0.00005596545],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8952676,0.07631101,0.002133397,0.002267851,0.000179637,0.000192494,0.00120046,0.00005560973,0.02239206],"genre_scores_gemma":[0.9718026,0.01965893,0.003124381,0.001246107,0.00009284339,0.00006296595,0.0005535236,0.0000193557,0.003439272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01194784,"threshold_uncertainty_score":0.0399695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0129196366165149,"score_gpt":0.2740662901394587,"score_spread":0.2611466535229438,"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."}}