{"id":"W4385384941","doi":"10.1177/26330040231188979","title":"The IRDiRC Chrysalis Task Force: making rare disease research attractive to companies","year":2023,"lang":"en","type":"article","venue":"Therapeutic Advances in Rare Disease","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; National Institute of Neurological Disorders and Stroke; Institut National de la Santé et de la Recherche Médicale; European Commission; Australian Government","keywords":"Task force; Task (project management); Investment (military); Disease; Public relations; Grant funding; Business; Psychology; Medicine; Political science; Marketing; Management; Economics; Pathology; Public administration","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003776763,0.0002037632,0.0001448425,0.0001156524,0.000350973,0.00006766585,0.0004464492,0.00004911434,0.00001202208],"category_scores_gemma":[0.0002353088,0.0001669852,0.00009693413,0.0005134885,0.0001562262,0.00001559838,0.0002409591,0.0001851893,0.00003530291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004117657,"about_ca_system_score_gemma":0.0001056022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003643912,"about_ca_topic_score_gemma":0.00004930279,"domain_scores_codex":[0.9981999,0.0001471745,0.0002133977,0.0004671153,0.0003617579,0.0006106567],"domain_scores_gemma":[0.9988096,0.0001617245,0.00003406718,0.0006155098,0.0001096936,0.0002694035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01412376,0.0007389478,0.04301742,0.001299202,0.0005777079,0.0009646339,0.005600677,0.3397968,0.1115347,0.00724415,0.0089455,0.4661565],"study_design_scores_gemma":[0.001781362,0.0003670009,0.2214925,0.0004502597,0.00009696002,0.000006230743,0.007034938,0.006574834,0.007698985,0.01474667,0.7383909,0.001359394],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8551292,0.1251436,0.01108239,0.003493247,0.001018462,0.00213099,0.0004524767,0.0002452788,0.00130434],"genre_scores_gemma":[0.9954897,0.002835239,0.00005587947,0.0002978465,0.0002430867,0.0002747481,0.0001107811,0.00005019545,0.0006424789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7294454,"threshold_uncertainty_score":0.6809459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03184840383005678,"score_gpt":0.4197062266891086,"score_spread":0.3878578228590518,"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."}}