{"id":"W3049148282","doi":"10.1111/medu.14352","title":"Stereoscopic three‐dimensional visualisation technology in anatomy learning: A meta‐analysis","year":2020,"lang":"en","type":"review","venue":"Medical Education","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Meta-analysis; CINAHL; MEDLINE; Stereoscopy; Critical appraisal; Confidence interval; Psychology; Data extraction; Computer science; Medical education; Medicine; Artificial intelligence; Nursing; Psychological intervention; Pathology; Alternative medicine; Internal medicine","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":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003547728,0.0003621537,0.002273633,0.001595235,0.00003690433,0.00001190195,0.0004071372,0.001353161,0.001325236],"category_scores_gemma":[0.0009269689,0.0003017327,0.0005781651,0.003490722,0.0001490633,0.00005429222,0.00008444385,0.001789853,0.0001475114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002167976,"about_ca_system_score_gemma":0.001155683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003128329,"about_ca_topic_score_gemma":0.0001056445,"domain_scores_codex":[0.9978688,0.0001312998,0.0007492725,0.0004429362,0.0004923732,0.000315348],"domain_scores_gemma":[0.9992013,0.0001559229,0.0001374469,0.0002753489,0.00003874058,0.0001912575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[5.506799e-7,0.00007252103,0.00001815343,0.001880509,0.01038687,0.00001891959,0.00002452642,0.00001104453,1.873015e-8,0.001853489,0.0006634137,0.98507],"study_design_scores_gemma":[0.00008122229,0.00002406746,0.000003161537,0.0005086415,0.03144632,0.00001805049,0.00002313401,0.003557125,4.420907e-7,0.0006389171,0.9634653,0.0002335936],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002370262,0.994836,0.001398478,0.002288688,0.0003588121,0.0003789064,0.000002829627,0.0005037607,0.0002087693],"genre_scores_gemma":[0.0006201013,0.9976858,0.0003679415,0.0002456756,0.0001231868,0.0005428797,0.000310286,0.00004654256,0.00005751932],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9848364,"threshold_uncertainty_score":0.9999435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03037609589443133,"score_gpt":0.3657897479624724,"score_spread":0.3354136520680411,"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."}}