{"id":"W4213035416","doi":"10.1038/s41598-022-06884-3","title":"Radiomics and machine learning for the diagnosis of pediatric cervical non-tuberculous mycobacterial lymphadenitis","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Mycobacterium research and diagnosis","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé; Fondation de l'Association des radiologistes du Québec","keywords":"Medicine; Tuberculous lymphadenitis; Cervical lymphadenopathy; Retrospective cohort study; Cervical lymph nodes; Radiology; Biopsy; Radiomics; Tuberculosis; Pathology; Dermatology; Internal medicine; Cancer","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004453715,0.0004762498,0.0004283834,0.001896472,0.0001830492,0.0006343866,0.0003664007,0.0004977538,0.0006780318],"category_scores_gemma":[0.011758,0.0001470185,0.0004524077,0.0006886707,0.0002961434,0.0005380122,0.0002843341,0.0006696366,0.0003037358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005051722,"about_ca_system_score_gemma":0.000751544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003612035,"about_ca_topic_score_gemma":0.004059345,"domain_scores_codex":[0.9982509,0.001139224,0.00009896664,0.0001579384,0.0002552835,0.00009764486],"domain_scores_gemma":[0.9957248,0.002999483,0.0005691991,0.0001810107,0.0003808,0.0001447056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006672874,0.0002112473,0.8240668,0.00006796893,0.0002182172,0.0003287824,0.0000664389,0.02228504,0.001812176,0.0004133431,0.001146497,0.1487163],"study_design_scores_gemma":[0.00008070471,0.001394692,0.4091547,0.000170959,0.0002847352,0.002462731,0.0002724673,0.5747117,0.00469571,0.003307387,0.003394215,0.00006997011],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9453993,0.008340258,0.04112172,0.001160971,0.00009612811,0.0001291474,0.0005941489,0.0002962214,0.002862098],"genre_scores_gemma":[0.9853854,0.0006565141,0.01333699,0.00008941778,0.00006032184,0.00002544555,0.0002390314,0.000009358627,0.0001976111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004453715,"threshold_uncertainty_score":0.02355379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02387438014200642,"score_gpt":0.2730835672097347,"score_spread":0.2492091870677283,"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."}}