{"id":"W2162227040","doi":"","title":"Getting it right: industry sponsorship and medical research.","year":2003,"lang":"en","type":"article","venue":"PubMed","topic":"Pharmaceutical industry and healthcare","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medical research; Public relations; Political science; Medicine; Medical education; Data science; Computer science; Pathology","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":[0.1089759,0.0008097844,0.001457282,0.002210102,0.006939795,0.02942078,0.001694154,0.03522136,0.01631225],"category_scores_gemma":[0.2097036,0.0007392094,0.0006566597,0.003205776,0.02557206,0.01907127,0.01237755,0.02545578,0.006847288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004344915,"about_ca_system_score_gemma":0.0285315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002453116,"about_ca_topic_score_gemma":0.004009156,"domain_scores_codex":[0.8864555,0.06024564,0.009427264,0.005589841,0.03156766,0.006714066],"domain_scores_gemma":[0.7434056,0.1430846,0.02117801,0.02009843,0.03098318,0.04125021],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001302151,0.00006386971,0.001387172,0.0003979456,0.0000420975,0.0002601664,0.001823374,0.00005779636,0.0004153462,0.4041927,0.4838403,0.107389],"study_design_scores_gemma":[0.00008028711,0.00007017127,0.001625067,0.001232378,0.00002983135,0.0004287478,0.001761356,0.0001158689,0.000258134,0.1358682,0.8584849,0.00004511566],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0008107749,0.05552465,0.001454679,0.8785378,0.01890673,0.00003119738,0.00005228227,0.00007324366,0.04460872],"genre_scores_gemma":[0.1273618,0.06405692,0.007910225,0.6487752,0.05818177,0.0003633591,0.0002434627,0.0003388799,0.09276846],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.8910241,"threshold_uncertainty_score":0.5763264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6568802361753315,"score_gpt":0.5854950040018495,"score_spread":0.07138523217348203,"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."}}