{"id":"W2000212771","doi":"10.1089/153056200750040156","title":"Evaluation of a Digital Camera for Acquiring Radiographic Images for Telemedicine Applications","year":2000,"lang":"en","type":"article","venue":"Telemedicine Journal and e-Health","topic":"Digital Imaging in Medicine","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Family Medicine","funders":"","keywords":"Telemedicine; Teleradiology; Radiography; Digital radiography; Image quality; Digital imaging; Medical diagnosis; Computed radiography; Computer science; Digital image; Medical physics; Artificial intelligence; Computer vision; Digital camera; Medicine; Multimedia; Image processing; Radiology; Health care; Image (mathematics)","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":[0.005110597,0.0002544998,0.0008775532,0.0004790537,0.0002681829,0.00003955164,0.0001282274,0.00005407182,0.0001607823],"category_scores_gemma":[0.0004187135,0.000187394,0.0001688534,0.0004431798,0.0003406052,0.0002720161,0.00001478931,0.0002822053,0.000002126741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001682096,"about_ca_system_score_gemma":0.0007009272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002684565,"about_ca_topic_score_gemma":0.000003068393,"domain_scores_codex":[0.9968418,0.00004478219,0.001155711,0.0003266085,0.001084284,0.0005468268],"domain_scores_gemma":[0.9974383,0.000199354,0.0005029793,0.0002697295,0.0009795574,0.0006100762],"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":[0.0002100538,0.0002442964,0.004407309,0.0007715126,0.0002114114,0.000002071243,0.0004389259,0.00001078214,0.0003968212,0.00006365527,0.05172895,0.9415142],"study_design_scores_gemma":[0.1067006,0.03737806,0.1034107,0.01096195,0.005477466,0.01103138,0.008247324,0.01835119,0.0008749994,0.0252461,0.6708917,0.001428591],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3398885,0.1135208,0.1749196,0.3311864,0.001133299,0.02545407,0.0005827937,0.0004131326,0.01290141],"genre_scores_gemma":[0.9773092,0.004794694,0.008847281,0.004585706,0.002661053,0.0004146682,0.0002782352,0.00007701009,0.001032158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9400856,"threshold_uncertainty_score":0.7641706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05374617418503532,"score_gpt":0.3868363421302669,"score_spread":0.3330901679452316,"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."}}