{"id":"W4311126711","doi":"10.2196/38783","title":"Phenotype Algorithms to Identify Hidradenitis Suppurativa Using Real-World Data: Development and Validation Study","year":2022,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Hidradenitis Suppurativa and Treatments","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Janssen Research and Development","keywords":"Hidradenitis suppurativa; Observational study; Algorithm; Medicine; Metric (unit); Machine learning; Computer science; Disease; Internal medicine","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":[0.03229063,0.00103262,0.0006183723,0.002365142,0.000408436,0.001841811,0.001654738,0.00113503,0.0006559976],"category_scores_gemma":[0.0796908,0.000356404,0.001213888,0.001243158,0.0004371983,0.00151657,0.001526551,0.001041174,0.0002565733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123301,"about_ca_system_score_gemma":0.002712555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003543544,"about_ca_topic_score_gemma":0.003141643,"domain_scores_codex":[0.9880387,0.00716677,0.001258581,0.001378437,0.001882784,0.0002746884],"domain_scores_gemma":[0.9454769,0.03708269,0.003444495,0.004219773,0.009094743,0.0006812797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002018459,0.003557124,0.6197703,0.0008671214,0.0017691,0.0003210699,0.0007326396,0.08338823,0.003208139,0.003108152,0.005856345,0.2754032],"study_design_scores_gemma":[0.001270169,0.00333432,0.173451,0.0003500203,0.000590031,0.0009707143,0.0005512254,0.8044786,0.006559153,0.003064793,0.005304284,0.00007571095],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8524461,0.00093957,0.1359781,0.0007227925,0.0001188257,0.002975529,0.003743169,0.001470501,0.001605589],"genre_scores_gemma":[0.7887087,0.0003328992,0.2023648,0.0002518085,0.00003145027,0.001541071,0.006449908,0.00008111631,0.0002382379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03229063,"threshold_uncertainty_score":0.1707712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1058890136209102,"score_gpt":0.4123365844317017,"score_spread":0.3064475708107915,"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."}}