{"id":"W2108731944","doi":"10.1186/1472-6947-13-45","title":"Developing model-based algorithms to identify screening colonoscopies using administrative health databases","year":2013,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; McGill University Health Centre; McGill University","funders":"Fonds de Recherche du Québec - Santé","keywords":"Medicine; Logistic regression; Algorithm; Health informatics; Database; Latent class model; Colonoscopy; Computer science; Machine learning; Data mining; Colorectal cancer; Public health; Cancer; Internal medicine; Nursing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007718902,0.0001833081,0.0003972412,0.0002733928,0.0003128915,0.0001505222,0.0001014783,0.00009252579,0.00007332781],"category_scores_gemma":[0.00084829,0.0001508068,0.00005781988,0.0003501458,0.00006939541,0.0003011984,0.0001776728,0.0002372329,0.00001403269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000132202,"about_ca_system_score_gemma":0.001219039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001182197,"about_ca_topic_score_gemma":0.0001210038,"domain_scores_codex":[0.9977701,0.00003299374,0.0007896741,0.0001945525,0.0008812469,0.0003313811],"domain_scores_gemma":[0.9981527,0.0007257,0.0002200046,0.0002124451,0.000190277,0.0004988553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001016857,0.00004103659,0.002106465,0.0005731658,0.00003154544,0.000009170352,0.001054917,0.009193317,0.00001971412,0.0002361876,0.0008696311,0.984848],"study_design_scores_gemma":[0.0008322285,0.0004960747,0.00243024,0.003111746,0.00001271286,0.00007504841,0.001463441,0.9908705,0.00009480556,0.0002429321,0.0002017163,0.0001685762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3543998,0.0001127965,0.6448262,0.0000950269,0.00009442763,0.0003399658,0.000005648795,0.00005006307,0.00007607677],"genre_scores_gemma":[0.3625604,0.00001564922,0.6346223,0.002699728,0.0000554465,0.00002259904,0.000009684836,0.000009869605,0.000004303467],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9846794,"threshold_uncertainty_score":0.6149724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2193496505047282,"score_gpt":0.4701982864607336,"score_spread":0.2508486359560054,"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."}}