{"id":"W3176597345","doi":"10.1096/fasebj.2018.32.1_supplement.504.5","title":"Design and Development of a Lumbar Puncture Simulation Model","year":2018,"lang":"en","type":"article","venue":"The FASEB Journal","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; Western University","funders":"","keywords":"Excellence; Flexibility (engineering); Cadaveric spasm; Medical physics; Lumbar; Medical simulation; Computer science; Medicine; Lumbar puncture; Parametric statistics; Simulation; Lumbar spine; Medical education; Physical therapy; Surgery; 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":[],"consensus_categories":[],"category_scores_codex":[0.0008647623,0.0005677206,0.0005694366,0.0007618379,0.0004052124,0.001278881,0.001900014,0.001299213,0.006856706],"category_scores_gemma":[0.001593593,0.0006312762,0.0008692212,0.0004159628,0.0005382767,0.0006146881,0.00111328,0.0007029125,0.001566731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005956455,"about_ca_system_score_gemma":0.001853489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002916307,"about_ca_topic_score_gemma":0.001482278,"domain_scores_codex":[0.9993634,0.0001220632,0.00007108768,0.00008959608,0.0002977499,0.00005619387],"domain_scores_gemma":[0.999342,0.0002058102,0.00005715105,0.00007933207,0.0002503949,0.00006517344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002808189,0.0001606789,0.003343799,0.0006208477,0.00004593529,0.0006566007,0.0004336184,0.8843307,0.03947891,0.009683274,0.002305625,0.05865925],"study_design_scores_gemma":[0.00007131459,0.0003193594,0.00104661,0.00008153661,0.00004462625,0.0002630719,0.0001130153,0.9668154,0.01075812,0.001692665,0.01874309,0.00005126438],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05419644,0.000293512,0.9259861,0.0004541258,0.0001863066,0.001371706,0.001099423,0.002402433,0.01401001],"genre_scores_gemma":[0.5338698,0.0007439897,0.4503264,0.0001259534,0.00003073566,0.002371017,0.001708869,0.0003266694,0.01049659],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006856706,"threshold_uncertainty_score":0.02293795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02410592057543593,"score_gpt":0.2400006791524737,"score_spread":0.2158947585770378,"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."}}