{"id":"W2735102775","doi":"10.1183/13993003.00953-2017","title":"Computer-aided reading of tuberculosis chest radiography: moving the research agenda forward to inform policy","year":2017,"lang":"en","type":"editorial","venue":"European Respiratory Journal","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"National Institute of Child Health and Human Development; National Institutes of Health; World Health Organization; Eunice Kennedy Shriver National Institute of Child Health and Human Development; United States Agency for International Development","keywords":"Medicine; Reading (process); Tuberculosis; Limit (mathematics); Key (lock); Radiography; Medical physics; Radiology; Pathology; Linguistics; Computer security","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":["metaresearch","metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.02725001,0.0006218521,0.001585588,0.003281631,0.002015939,0.0004783609,0.002515479,0.0005520657,0.0001040188],"category_scores_gemma":[0.0303996,0.0004223091,0.001080133,0.001158471,0.0008732424,0.0002876751,0.001252146,0.007590609,0.0003297741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006426636,"about_ca_system_score_gemma":0.002536842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002158512,"about_ca_topic_score_gemma":0.00003104622,"domain_scores_codex":[0.9880087,0.004189409,0.00196898,0.0007823147,0.003201402,0.00184913],"domain_scores_gemma":[0.9898003,0.003051629,0.001086684,0.002184431,0.002422299,0.001454658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005146105,0.00007425531,0.001061401,0.0003158742,0.0007331746,0.0007828095,0.0006242714,0.00002694036,0.0007444078,0.00002913817,0.9589642,0.03612897],"study_design_scores_gemma":[0.001313333,0.002112377,0.01505242,0.001519828,0.0001130817,0.0001977992,0.0001039699,0.00002076245,0.0001121324,0.00003394866,0.9790831,0.000337234],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.03847241,0.008736538,0.0008662315,0.0309438,0.7425063,0.004471925,0.0005592484,0.0003001802,0.1731434],"genre_scores_gemma":[0.007206501,0.002429479,0.0009860732,0.002260242,0.9851766,0.00002484245,0.00005589643,0.0002846651,0.001575722],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.2426703,"threshold_uncertainty_score":0.9998229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09697785568121002,"score_gpt":0.4123856619197552,"score_spread":0.3154078062385452,"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."}}