{"id":"W4402335851","doi":"10.1101/2024.09.06.24313068","title":"Leveraging Machine Learning & Mobile Application Technology for Vitiligo Management: A Proof-of-Concept","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"melanin and skin pigmentation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; North York General Hospital; SKiN Health","funders":"","keywords":"Vitiligo; Proof of concept; Computer science; Artificial intelligence; Human–computer interaction; Medicine; Dermatology; Operating system","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":[0.0005798946,0.0006428864,0.0003752944,0.0003100182,0.0001608886,0.0007114792,0.0008100387,0.0008645279,0.003331165],"category_scores_gemma":[0.0008404516,0.0001749331,0.0003596231,0.0001269433,0.0002107482,0.0007137902,0.000612799,0.0007022116,0.001457606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002612439,"about_ca_system_score_gemma":0.0003977142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007458421,"about_ca_topic_score_gemma":0.0007118169,"domain_scores_codex":[0.9995628,0.0000475855,0.00001504251,0.0000733486,0.0002275095,0.00007380891],"domain_scores_gemma":[0.9997317,0.00004476016,0.00003091219,0.00002516818,0.0001139928,0.0000535428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006409016,0.001168652,0.004334561,0.001416059,0.000201799,0.002360671,0.000187114,0.01472846,0.6138105,0.002918661,0.0285109,0.3297217],"study_design_scores_gemma":[0.0002484661,0.003747875,0.006600392,0.0002155233,0.0001487703,0.002273267,0.000129947,0.1944422,0.7025689,0.001383283,0.08807798,0.000163309],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5383704,0.006301566,0.3952411,0.005391107,0.002427498,0.002434943,0.002794044,0.0155057,0.03153355],"genre_scores_gemma":[0.791743,0.001725122,0.190607,0.001688214,0.0002219079,0.001001626,0.001169428,0.0003507424,0.01149297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003331165,"threshold_uncertainty_score":0.0111438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282472929998164,"score_gpt":0.281250163226852,"score_spread":0.2684254339268703,"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."}}