{"id":"W2892218058","doi":"10.1002/mp.13136","title":"Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology","year":2018,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Cancer Institute","keywords":"Radiation oncology; Big data; Data science; Medical physics; Medicine; Medical education; Computer science; Internal medicine; Radiation therapy; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08541719,0.001288176,0.00219701,0.003480294,0.002653615,0.01299136,0.003654738,0.004144769,0.003574914],"category_scores_gemma":[0.1924089,0.001092976,0.001459605,0.009083724,0.006342578,0.02346842,0.008870274,0.0103142,0.001249695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004218804,"about_ca_system_score_gemma":0.01047024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006558577,"about_ca_topic_score_gemma":0.008143602,"domain_scores_codex":[0.9540461,0.02935022,0.003465169,0.003280986,0.008600588,0.001256945],"domain_scores_gemma":[0.6724033,0.2498073,0.0153195,0.02412744,0.02817962,0.01016289],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007553534,0.000752444,0.07952382,0.005059782,0.0008340525,0.0005948708,0.006319215,0.05069067,0.00177944,0.2785148,0.1578352,0.4173404],"study_design_scores_gemma":[0.000103477,0.0002054896,0.01999381,0.002487115,0.000159647,0.0003268271,0.008829301,0.0657594,0.001410507,0.8046628,0.09583285,0.000228802],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0406608,0.02678663,0.2372678,0.6606008,0.002714805,0.0008798565,0.007734851,0.001298666,0.02205569],"genre_scores_gemma":[0.5476078,0.02123843,0.3741512,0.03689217,0.007742809,0.001572467,0.00767466,0.0008811115,0.002239393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9145828,"threshold_uncertainty_score":0.4517347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09679100752363538,"score_gpt":0.4264316811514313,"score_spread":0.3296406736277959,"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."}}