{"id":"W4313645291","doi":"10.21203/rs.3.rs-2423270/v1","title":"PILLAR: ParaspInaL muscLe segmentAtion pRoject - a comprehensive online resource to guide manual segmentation of paraspinal muscles from Magnetic Resonance Imaging","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fonds de Recherche du Québec - Santé; Concordia University; Réseau en Bio-Imagerie du Quebec","keywords":"Lumbar; Magnetic resonance imaging; Medicine; Segmentation; Low back pain; Back pain; Erector spinae muscles; Pillar; Multifidus muscle; Physical medicine and rehabilitation; Radiology; Computer science; Artificial intelligence; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003964009,0.00314544,0.001423353,0.004782066,0.0009392089,0.002386005,0.003274014,0.002482618,0.1054362],"category_scores_gemma":[0.01533333,0.001695644,0.001698946,0.001582035,0.0006392755,0.002350742,0.005216853,0.00188358,0.09020805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004772732,"about_ca_system_score_gemma":0.002552134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002969612,"about_ca_topic_score_gemma":0.004587832,"domain_scores_codex":[0.9983551,0.000368695,0.0002590267,0.0003703918,0.0005125031,0.0001344078],"domain_scores_gemma":[0.9957008,0.001725354,0.0003195502,0.0006837423,0.001215777,0.0003548134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005824763,0.00009175316,0.002564384,0.003457861,0.0002331106,0.0005654385,0.0003658905,0.001397805,0.008073731,0.001674229,0.8247488,0.1562445],"study_design_scores_gemma":[0.0007318384,0.0002814329,0.01949546,0.003906453,0.0003276286,0.004283209,0.0004607102,0.0301093,0.02520676,0.01900443,0.8957438,0.0004489341],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.008791114,0.005370058,0.328355,0.001208601,0.0009696716,0.00170348,0.2668985,0.3689793,0.01772423],"genre_scores_gemma":[0.02865987,0.002307929,0.4780343,0.00151201,0.0003915752,0.006038927,0.3404081,0.1282105,0.01443675],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1054362,"threshold_uncertainty_score":0.3527191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0792981678649665,"score_gpt":0.4422130266384807,"score_spread":0.3629148587735141,"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."}}