{"id":"W4408011571","doi":"10.31586/gjmcr.2023.1289","title":"Leveraging AI, ML, and Generative Neural Models to Bridge Gaps in Genetic Therapy Access and Real-Time Resource Allocation","year":2023,"lang":"en","type":"article","venue":"Global Journal of Medical Case Reports","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Bridge (graph theory); Computer science; Generative grammar; Resource allocation; Resource (disambiguation); Artificial intelligence; Machine learning; Medicine; Computer network; Internal medicine","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":[0.002484473,0.001496856,0.001351361,0.0009980258,0.0004653592,0.002721852,0.002311387,0.002058337,0.003689371],"category_scores_gemma":[0.0107579,0.0008763106,0.0006679374,0.001134456,0.001632508,0.002884025,0.002133216,0.003492659,0.0007136369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001865922,"about_ca_system_score_gemma":0.001844085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01048621,"about_ca_topic_score_gemma":0.009387436,"domain_scores_codex":[0.9990178,0.0003858068,0.00005156528,0.0002210857,0.000205724,0.0001180033],"domain_scores_gemma":[0.9925895,0.006173886,0.0004399683,0.0003257743,0.0003093719,0.0001615276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006497915,0.00004219211,0.0008347909,0.00007138469,0.00004539426,0.00007322354,0.00005507393,0.9411556,0.00037052,0.02284423,0.00147566,0.032967],"study_design_scores_gemma":[0.000004229878,0.00000832622,0.00004690492,0.000005850126,0.000005158536,0.00001142717,0.000005295769,0.985064,0.00009128892,0.01430164,0.0004513483,0.000004632406],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01381922,0.001950114,0.9751168,0.002215082,0.00014321,0.00004241463,0.0001978876,0.0008435153,0.005671804],"genre_scores_gemma":[0.8446146,0.002433283,0.1437563,0.0009164684,0.0004967555,0.0001865557,0.0004930763,0.0003065081,0.006796507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01048621,"threshold_uncertainty_score":0.02085036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02954319826877667,"score_gpt":0.2982935775827674,"score_spread":0.2687503793139907,"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."}}