{"id":"W1970477049","doi":"10.1016/j.ajodo.2008.07.018","title":"Intraexaminer and interexaminer reliabilities of landmark identification on digitized lateral cephalograms and formatted 3-dimensional cone-beam computerized tomography images","year":2010,"lang":"en","type":"article","venue":"American Journal of Orthodontics and Dentofacial Orthopedics","topic":"Orthodontics and Dentofacial Orthopedics","field":"Dentistry","cited_by":174,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Landmark; Cone beam computed tomography; Orthodontics; Radiography; Cephalometry; Medicine; Tomography; Anatomical landmark; Dentistry; Computed tomography; Anatomy; Artificial intelligence; Computer science; Radiology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001270015,0.0004260768,0.001066151,0.0005177672,0.0001660172,0.0003208707,0.0002059935,0.0001780388,0.00002291831],"category_scores_gemma":[0.0004201797,0.0003540593,0.0002553952,0.0003820593,0.001383498,0.0004474886,0.0002091959,0.0006730717,0.00000178523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001344384,"about_ca_system_score_gemma":0.00007218727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001224489,"about_ca_topic_score_gemma":0.00008260569,"domain_scores_codex":[0.9967414,0.0001747229,0.001529749,0.0004106418,0.0007409137,0.0004026138],"domain_scores_gemma":[0.9967201,0.0003964684,0.001427967,0.0003284873,0.0006771623,0.0004498464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007212995,0.0002321366,0.9442379,0.0001016529,0.0002450438,0.0001377279,0.0004774765,0.000008300063,0.01071241,0.0007685377,0.0002569785,0.04210058],"study_design_scores_gemma":[0.003450403,0.001552215,0.9873488,0.0001376583,0.0003562739,0.000942873,0.0003411474,0.0004091695,0.0006622571,0.0002135834,0.004036132,0.0005495136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957132,0.0002104756,0.001972893,0.0001746889,0.001333423,0.0002516424,0.0001708423,0.00003454965,0.0001382369],"genre_scores_gemma":[0.9938542,0.0004961321,0.004828183,0.0002633822,0.0002946298,0.000004172225,0.00004012249,0.0000478411,0.0001713226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04311091,"threshold_uncertainty_score":0.9998912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00778630791497953,"score_gpt":0.2496368847021263,"score_spread":0.2418505767871468,"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."}}