{"id":"W6902317661","doi":"10.6084/m9.figshare.26664010.v1","title":"Additional file 3 of Studying missingness in spinal cord injury data: challenges and impact of data imputation","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Praxis Spinal Cord Institute; University of Saskatchewan","funders":"","keywords":"Missing data; Spinal cord injury; Imputation (statistics); Data collection; MEDLINE","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009898728,0.0008941154,0.001121311,0.003335762,0.001174037,0.002398596,0.002432331,0.001762843,0.8391821],"category_scores_gemma":[0.1527439,0.0006210083,0.00124648,0.005847789,0.0004038529,0.002257968,0.001712531,0.001541853,0.09513266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001356127,"about_ca_system_score_gemma":0.003793434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01128931,"about_ca_topic_score_gemma":0.01600554,"domain_scores_codex":[0.9960758,0.001586335,0.0007715765,0.0005890732,0.0007029343,0.0002742022],"domain_scores_gemma":[0.7702887,0.2011806,0.006985947,0.007789717,0.01227277,0.001482241],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002490186,0.00005023332,0.002531484,0.001568904,0.00006532374,0.0000577095,0.00007376949,0.0004876503,0.00003042615,0.001124144,0.9860895,0.007671861],"study_design_scores_gemma":[0.006580837,0.0003022331,0.0317637,0.008662222,0.0004884753,0.0008202553,0.0009172854,0.004487424,0.0006996705,0.03060621,0.9144386,0.0002331373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.00022572,0.00002657052,0.0006739382,0.0003812716,0.00003647877,0.0001608756,0.9968449,0.0002800737,0.001370201],"genre_scores_gemma":[0.01898397,0.0003051003,0.01334806,0.002321221,0.0002404536,0.005293156,0.9424456,0.001645591,0.01541673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9901013,"threshold_uncertainty_score":0.2293871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3797826983178341,"score_gpt":0.4961138411383679,"score_spread":0.1163311428205338,"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."}}