{"id":"W2620986503","doi":"","title":"Pharmaceutical M&A Activity: Effects on Prices, Innovation, and Competition","year":2017,"lang":"en","type":"article","venue":"","topic":"Pharmaceutical Economics and Policy","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Upstream (networking); Downstream (manufacturing); Competition (biology); Pharmaceutical industry; Harm; Industrial organization; Business; Market concentration; Marketing; Politics; Product (mathematics); Distribution (mathematics); Market structure; Political science; Biotechnology; Engineering","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":[0.003133948,0.0001910943,0.0004393231,0.001151447,0.001339913,0.005973695,0.0006009217,0.002144394,0.01568886],"category_scores_gemma":[0.02136309,0.0002031924,0.0006944802,0.001724684,0.002183252,0.002813537,0.002336099,0.001791387,0.001108158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003213257,"about_ca_system_score_gemma":0.002526401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006063648,"about_ca_topic_score_gemma":0.006480801,"domain_scores_codex":[0.9969358,0.001564005,0.0001341956,0.0002419743,0.000549064,0.0005749275],"domain_scores_gemma":[0.9624348,0.02433685,0.00718407,0.0005994054,0.001655742,0.003789131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004080177,0.004349108,0.7412695,0.0003719235,0.0003997062,0.002017768,0.002558199,0.009351616,0.004577084,0.143194,0.006570491,0.08126041],"study_design_scores_gemma":[0.0004025742,0.002746482,0.9007644,0.0001221123,0.0003501293,0.0005508586,0.005915164,0.01335126,0.002921231,0.06070539,0.01206882,0.0001015829],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9216202,0.00147312,0.0006073563,0.005712545,0.00004348172,0.00004435074,0.000267462,0.00002017653,0.07021116],"genre_scores_gemma":[0.997481,0.0002993717,0.00008098248,0.0001908216,0.00005291592,0.00001012894,0.00003969241,0.000003850554,0.001841159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01568886,"threshold_uncertainty_score":0.05248451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08957918890393224,"score_gpt":0.3404639866273215,"score_spread":0.2508847977233892,"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."}}