{"id":"W4283215073","doi":"10.2196/33833","title":"Using Implementation Science to Understand Teledermatology Implementation Early in the COVID-19 Pandemic: Cross-sectional Study","year":2022,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Fogarty International Center; National Institutes of Health","keywords":"Teledermatology; Pandemic; Coronavirus disease 2019 (COVID-19); Medicine; Telemedicine; MEDLINE; Medical education; Nursing; Medical emergency; Health care; Pathology; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008417857,0.0001341229,0.0002117062,0.0007119355,0.0009282627,0.00008197189,0.0002396166,0.00003562804,0.0007613879],"category_scores_gemma":[0.00003601849,0.0001211186,0.00004528112,0.001003169,0.0001457487,0.0001102582,0.0002261679,0.0002512429,0.00001560195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091742,"about_ca_system_score_gemma":0.0004080193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000975944,"about_ca_topic_score_gemma":0.001832382,"domain_scores_codex":[0.9977124,0.0003544479,0.0005112254,0.0004293843,0.0005854359,0.0004070435],"domain_scores_gemma":[0.9993033,0.0001040021,0.0001371889,0.0002622622,0.00005901657,0.0001342782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000219412,0.0002028373,0.9874083,0.00002016421,0.00002503021,0.0004266298,0.008390686,0.0001540621,0.001626892,0.0007358807,0.000503642,0.000286504],"study_design_scores_gemma":[0.004142413,0.0006187987,0.862349,8.407971e-7,0.00003212411,0.02448378,0.09921584,0.000150037,0.0001184344,0.0002388651,0.008488526,0.0001613678],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933429,0.000009815995,0.001161573,0.002067242,0.0003441742,0.002821238,0.00001219839,0.0000644269,0.0001763764],"genre_scores_gemma":[0.992848,0.000001097892,0.000103043,0.006518254,0.00002960952,0.0004359835,0.00002352756,0.00001216477,0.00002836741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1250593,"threshold_uncertainty_score":0.8336664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1527144507498607,"score_gpt":0.4836864901610005,"score_spread":0.3309720394111397,"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."}}