{"id":"W4406506544","doi":"10.1016/j.yort.2014.12.019","title":"10.1016/j.yort.2014.12.019","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Medicine; Propensity score matching; Osteosarcoma; Matching (statistics); Oncology; Surgery; Internal medicine; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007022755,0.001507069,0.0005781642,0.002094115,0.000683195,0.003327689,0.001044035,0.003074735,0.8954217],"category_scores_gemma":[0.001577765,0.0005242792,0.0007455013,0.001269698,0.001127743,0.002395601,0.001436773,0.001188295,0.8746434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006147148,"about_ca_system_score_gemma":0.0008470793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002324356,"about_ca_topic_score_gemma":0.001773819,"domain_scores_codex":[0.9996709,0.00002590278,0.00002398546,0.0001176665,0.0001035696,0.00005809989],"domain_scores_gemma":[0.9991447,0.0002283616,0.0001326902,0.0001212933,0.0001705395,0.0002023686],"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":[0.0001775806,0.0001902505,0.002376818,0.0003856099,0.0000482341,0.0002534791,0.00007665497,0.001410704,0.002406893,0.008040723,0.214312,0.7703211],"study_design_scores_gemma":[0.00007856407,0.0001502917,0.002905549,0.0006980297,0.00005890712,0.001652484,0.0002231098,0.004187294,0.0018409,0.0107198,0.9774419,0.0000431206],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.007266159,0.01158833,0.03763134,0.005968252,0.002722577,0.0001753245,0.005717317,0.01069907,0.9182317],"genre_scores_gemma":[0.01668414,0.002935826,0.01016383,0.0009882734,0.0005834297,0.00008455319,0.002713,0.0007622283,0.9650847],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1045783,"threshold_uncertainty_score":0.1491681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004642020398262207,"score_gpt":0.2117918470468495,"score_spread":0.2071498266485873,"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."}}