{"id":"W4220762082","doi":"10.2196/33006","title":"Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Rasch model; Machine learning; Artificial intelligence; Receiver operating characteristic; Naive Bayes classifier; Respondent; Logistic regression; Computer science; Population; Computerized adaptive testing; Item response theory; Bayes' theorem; Statistics; Data mining; Bayesian probability; Mathematics; Medicine; Support vector machine; Psychometrics","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":[0.01539691,0.0006941235,0.0005078434,0.0009466936,0.0003829534,0.0009649589,0.001235135,0.000749804,0.002634223],"category_scores_gemma":[0.0446994,0.0005325777,0.0009672204,0.0007013518,0.0005399141,0.001324252,0.001036651,0.001132604,0.0007479856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001507932,"about_ca_system_score_gemma":0.001611611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009558613,"about_ca_topic_score_gemma":0.008370086,"domain_scores_codex":[0.9936667,0.004741601,0.0002053309,0.0004312739,0.000789582,0.0001654775],"domain_scores_gemma":[0.9566168,0.03372504,0.00152824,0.003115543,0.004423825,0.0005905902],"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":[0.002289343,0.01330212,0.6954826,0.0004044975,0.0005312428,0.0007641139,0.00510312,0.04905537,0.001365033,0.003032285,0.002907761,0.2257625],"study_design_scores_gemma":[0.0004490053,0.01042246,0.2859256,0.0002703485,0.0003767748,0.0009023997,0.002171158,0.690144,0.003355886,0.002643671,0.003186146,0.000152541],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9879575,0.00007001726,0.01002843,0.000102406,0.000009421003,0.0005912271,0.0001745317,0.0001180902,0.0009483648],"genre_scores_gemma":[0.9722062,0.000123568,0.02602561,0.00004961292,0.000007022128,0.0005194512,0.000388573,0.00003236321,0.000647517],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01539691,"threshold_uncertainty_score":0.08142763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04987847350160329,"score_gpt":0.3487782968916951,"score_spread":0.2988998233900918,"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."}}