{"id":"W4412834058","doi":"10.2196/65811","title":"Deep Learning–Based Pattern Recognition for Detecting Penile Abnormalities: Protocol for Developing a Mobile App for Circumcision Eligibility","year":2025,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Genital Health and Disease","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Protocol (science); Medicine; Computer science; Artificial intelligence; Psychology; World Wide Web; Alternative medicine; Pathology","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":[0.004454489,0.001469668,0.001083284,0.0007905493,0.000882513,0.0009354054,0.001460403,0.001566843,0.03416735],"category_scores_gemma":[0.007654791,0.0008052364,0.0009315494,0.0003469543,0.0007744977,0.0006688369,0.00118781,0.001724468,0.01034481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000637615,"about_ca_system_score_gemma":0.003111823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001272553,"about_ca_topic_score_gemma":0.002180208,"domain_scores_codex":[0.9983881,0.0005730898,0.0002445401,0.0003087502,0.000336297,0.0001492387],"domain_scores_gemma":[0.9969941,0.0008352968,0.0001439963,0.0005141091,0.00131457,0.0001978335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.02303477,0.01697871,0.01464362,0.01201209,0.0004658696,0.004780738,0.002426589,0.01706199,0.1587803,0.006679045,0.104079,0.6390573],"study_design_scores_gemma":[0.01509907,0.03989325,0.06092818,0.005818406,0.0009929485,0.007297421,0.0019485,0.06078012,0.2237362,0.01535129,0.5672441,0.0009105198],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.09975624,0.001888795,0.2287882,0.001623046,0.0009711978,0.6268908,0.01781985,0.006110819,0.01615093],"genre_scores_gemma":[0.04717806,0.001170508,0.1540458,0.001372586,0.0001276713,0.7799093,0.006247629,0.0002862962,0.009662161],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.03416735,"threshold_uncertainty_score":0.1143011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2668069944468811,"score_gpt":0.5936797486245597,"score_spread":0.3268727541776786,"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."}}