{"id":"W4386267790","doi":"10.1123/jsr.2022-0453","title":"Intrarater and Interrater Reliability and Agreement of a Method to Quantify Lower-Extremity Kinematics Using Remote Data Collection","year":2023,"lang":"en","type":"article","venue":"Journal of Sport Rehabilitation","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Intraclass correlation; Inter-rater reliability; Intra-rater reliability; Sagittal plane; Coronal plane; Kinematics; Orthodontics; Reliability (semiconductor); Pearson product-moment correlation coefficient; Mathematics; Computer science; Physical medicine and rehabilitation; Medicine; Statistics; Reproducibility; Physics; Confidence interval; Anatomy","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":[0.05800915,0.0007908285,0.0007699794,0.002167544,0.001079239,0.001552245,0.001028128,0.0008304434,0.001541271],"category_scores_gemma":[0.09994051,0.0004393997,0.001139438,0.0008866378,0.001480991,0.001100089,0.002687748,0.0008496229,0.0009109505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005457466,"about_ca_system_score_gemma":0.001412349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001299993,"about_ca_topic_score_gemma":0.00309155,"domain_scores_codex":[0.9427473,0.03028692,0.006053056,0.006416206,0.01364767,0.000848835],"domain_scores_gemma":[0.8790632,0.0553252,0.01002309,0.0105826,0.04365385,0.001352009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002823629,0.0008392276,0.698383,0.001861307,0.002180049,0.0003475383,0.02248443,0.005513167,0.03681592,0.002369787,0.003852462,0.2225294],"study_design_scores_gemma":[0.0003321033,0.003736819,0.8991304,0.0007483779,0.000787571,0.001396527,0.00740441,0.04264696,0.03254554,0.003654275,0.007300855,0.0003161544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8108072,0.001271044,0.1747472,0.0001866403,0.0003755219,0.001929639,0.0007064011,0.0004720246,0.009504378],"genre_scores_gemma":[0.9424348,0.0001670587,0.05442643,0.00007427212,0.00006891843,0.001306969,0.0002844417,0.0001044653,0.001132732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05800915,"threshold_uncertainty_score":0.3067853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0433876521593357,"score_gpt":0.3845783785603656,"score_spread":0.3411907264010299,"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."}}