{"id":"W4408225269","doi":"10.58931/cdt.2025.61136","title":"Tattoo Regret? Principles and Pearls to Optimize Laser Tattoo Removal","year":2025,"lang":"en","type":"article","venue":"Canadian dermatology today.","topic":"Tattoo and Body Piercing Complications","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SKiN Health","funders":"","keywords":"Regret; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002372341,0.000134474,0.0002302233,0.0003643752,0.0009959901,0.0001132609,0.0003501669,0.0001860267,0.0001708628],"category_scores_gemma":[0.0005148322,0.0001484195,0.00004337325,0.0005242659,0.0004385373,0.0001141162,0.00006114377,0.000168446,0.0001587091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001990564,"about_ca_system_score_gemma":0.001423719,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05640231,"about_ca_topic_score_gemma":0.4852608,"domain_scores_codex":[0.9985267,0.0001804248,0.0002256518,0.0003550197,0.0001102067,0.0006020329],"domain_scores_gemma":[0.9986369,0.0002197481,0.00004274706,0.0003256218,0.0001154553,0.0006595312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001618229,0.00002723307,0.04208527,0.00003293804,0.00007047472,0.0003710538,0.007746278,0.00007754417,0.00005232547,0.5977657,0.3432029,0.008552098],"study_design_scores_gemma":[0.0001826932,0.000006126996,0.01907041,0.00003137351,0.00002196461,0.00007052052,0.001148337,0.00006845305,0.00005266387,0.001555236,0.9776151,0.0001771558],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.331221,0.0006271935,0.0006921245,0.2393224,0.0007975238,0.0007182182,0.00005673274,0.0001863885,0.4263783],"genre_scores_gemma":[0.9515305,0.00006705653,0.004911372,0.01087001,0.00008767578,0.00006202279,0.0000143647,0.00001553414,0.03244154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6344122,"threshold_uncertainty_score":0.9498812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02376612112252252,"score_gpt":0.301619187104701,"score_spread":0.2778530659821785,"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."}}