{"id":"W3121980999","doi":"10.2139/ssrn.3353887","title":"Medical Innovation, Education, and Labor Market Outcomes of Cancer Patients","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Pharmaceutical Economics and Policy","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Labour economics; Business; Cancer; Economics; Medicine; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.001394603,0.0001616244,0.0003298966,0.001221148,0.0006855024,0.00193148,0.000509644,0.001576272,0.0122194],"category_scores_gemma":[0.01148425,0.0001772445,0.0008040748,0.001630133,0.0005332248,0.001277523,0.0009962437,0.001828436,0.001015316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001127873,"about_ca_system_score_gemma":0.001599935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01363698,"about_ca_topic_score_gemma":0.0145431,"domain_scores_codex":[0.9989219,0.000259857,0.00009607479,0.00008040182,0.0001022782,0.0005395146],"domain_scores_gemma":[0.9899394,0.00265353,0.00423115,0.0002218891,0.0004955961,0.002458433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004608123,0.0006234901,0.9930669,0.00001901846,0.00007230573,0.00007016899,0.0001793587,0.0003789563,0.00005703839,0.0003409946,0.0006797021,0.004051221],"study_design_scores_gemma":[0.00005098151,0.0003465891,0.9960967,0.00002776992,0.00005446719,0.00007850975,0.000932129,0.0008412579,0.000069539,0.0007431509,0.0007424217,0.00001657573],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935621,0.0006305119,0.00003606534,0.002022011,0.00002593745,0.00001153893,0.0008466921,0.000004118592,0.002861082],"genre_scores_gemma":[0.998431,0.000167652,0.00000993368,0.0001713881,0.00004397097,0.000004797424,0.0004237421,0.000001347502,0.00074611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01363698,"threshold_uncertainty_score":0.04087794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01111448746374966,"score_gpt":0.2873216248207831,"score_spread":0.2762071373570334,"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."}}