{"id":"W2166116545","doi":"10.1016/j.socscimed.2008.01.010","title":"How pharmaceutical industry funding affects trial outcomes: Causal structures and responses","year":2008,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Pharmaceutical industry and healthcare","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":221,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Pharmaceutical industry; Context (archaeology); Clinical trial; Conflict of interest; Publication bias; Drug industry; Business; Public relations; Public economics; Economics; MEDLINE; Medicine; Political science; Finance; Law; Engineering; Pharmacology; Engineering ethics","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":["metaresearch"],"category_scores_codex":[0.1986765,0.00107802,0.002953944,0.003705117,0.002205835,0.007358027,0.003221513,0.01001345,0.01459353],"category_scores_gemma":[0.6223775,0.00127685,0.005843145,0.00530351,0.006759397,0.006344812,0.004650086,0.009594211,0.0009643973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003314867,"about_ca_system_score_gemma":0.00830452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01122366,"about_ca_topic_score_gemma":0.007461388,"domain_scores_codex":[0.7278105,0.2191173,0.01305685,0.01691011,0.01193759,0.01116761],"domain_scores_gemma":[0.09286779,0.8210652,0.05465529,0.02072161,0.007070642,0.00361942],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006560316,0.001142661,0.9245012,0.0007025762,0.00969416,0.000343812,0.002185213,0.002255761,0.0002108585,0.0228132,0.004650328,0.02493989],"study_design_scores_gemma":[0.003698658,0.001737909,0.6908311,0.0009053939,0.02075771,0.0006523795,0.003535562,0.02139121,0.001668235,0.246302,0.008285004,0.0002348284],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7922894,0.01515797,0.03311101,0.1288373,0.00108473,0.001477949,0.004331226,0.0002858481,0.02342448],"genre_scores_gemma":[0.9904844,0.0006950332,0.002036704,0.004224053,0.0005461136,0.0003622797,0.0003695385,0.00004714107,0.001234802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8013235,"threshold_uncertainty_score":0.9881745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5528880235862371,"score_gpt":0.5954428506206726,"score_spread":0.04255482703443547,"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."}}