{"id":"W2757851334","doi":"10.2174/2213476x03666160112001136","title":"Product Life Cycle Management for Pharmaceutical Innovation","year":2015,"lang":"en","type":"article","venue":"Applied Clinical Research Clinical Trials and Regulatory Affairs","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scrutiny; Commercialization; Product lifecycle; Business; Product (mathematics); New product development; Scope (computer science); Biosimilar; Product life-cycle management; Phase (matter); Competition (biology); Pharmaceutical industry; Marketing; Industrial organization; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007628419,0.001212774,0.0007919778,0.00305905,0.00105221,0.00570459,0.001857591,0.001347421,0.01506763],"category_scores_gemma":[0.01763003,0.0004732607,0.001218472,0.003605137,0.001421955,0.004380174,0.002447227,0.001617204,0.004168377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00566466,"about_ca_system_score_gemma":0.00649787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003178582,"about_ca_topic_score_gemma":0.001807455,"domain_scores_codex":[0.9940652,0.001829756,0.0003792175,0.0008783388,0.002531707,0.0003158662],"domain_scores_gemma":[0.9898051,0.003728059,0.001677046,0.001132478,0.003334252,0.0003230783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001983678,0.0001580172,0.003480957,0.00216675,0.0000781181,0.0001193008,0.0004332515,0.05014383,0.002112024,0.2285069,0.02996483,0.6826377],"study_design_scores_gemma":[0.00008532751,0.0006018149,0.004294951,0.002030063,0.0001521047,0.0002755067,0.000767758,0.1953465,0.007794584,0.3466364,0.441856,0.0001591188],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01913521,0.02950407,0.767867,0.009347679,0.0009896737,0.002493344,0.002717766,0.003376525,0.1645688],"genre_scores_gemma":[0.4872212,0.01915055,0.4502811,0.001561902,0.0006387526,0.002646035,0.004645577,0.0006478407,0.03320703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01506763,"threshold_uncertainty_score":0.05040628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6685465453233798,"score_gpt":0.6240170960708625,"score_spread":0.04452944925251723,"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."}}