{"id":"W4324130770","doi":"10.1101/2023.03.14.532596","title":"Mapping the landscape of magnetic field effects on neural regeneration and repair: a systematic review, mathematical model, and meta-analysis","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Electromagnetic Fields and Biological Effects","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"Army Research Laboratory; Army Research Office","keywords":"Regeneration (biology); Regenerative medicine; Meta-analysis; Magnetic field; Field (mathematics); Computer science; Neuroscience; Mathematical model; Biology; Biological system; Physics; Statistics; Medicine; Mathematics; Cell biology; Pathology; Stem cell","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":[0.0008729888,0.0003539917,0.001210983,0.00009811313,0.00007720131,0.00005091818,0.0002226431,0.0003904698,0.000007733622],"category_scores_gemma":[0.0008587875,0.000213779,0.000515627,0.000264205,0.00006415574,0.000003071138,0.0003133536,0.0002933717,0.000001712446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006597073,"about_ca_system_score_gemma":0.00003683034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005620572,"about_ca_topic_score_gemma":0.000002474859,"domain_scores_codex":[0.9980232,0.0003670379,0.000505336,0.000658934,0.0002086539,0.0002368955],"domain_scores_gemma":[0.9983135,0.0002714501,0.0002889445,0.000915506,0.0001250012,0.00008554422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.00003882467,0.00008777541,0.0002089473,0.1529318,0.01878119,0.00001729009,0.00001044491,0.0003131634,0.8261192,0.0005291454,0.0009610918,0.000001074359],"study_design_scores_gemma":[0.001213522,0.01144307,0.004474142,0.01278597,0.405809,7.136568e-7,0.00001123827,0.1665756,0.3944918,0.0001566838,0.00002288677,0.003015463],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.907151,0.08534147,0.002061984,0.002227475,0.00007388545,0.002959344,0.00006293445,0.0001136994,0.000008189731],"genre_scores_gemma":[0.9936249,0.003632848,0.0009609918,0.001177861,0.0000608378,0.0004805072,0.000001116873,0.00003014257,0.00003081499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4316275,"threshold_uncertainty_score":0.8717654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02248639722086559,"score_gpt":0.232597666499482,"score_spread":0.2101112692786164,"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."}}