{"id":"W2893114893","doi":"10.1016/j.compmedimag.2018.09.006","title":"3D imaging system for respiratory monitoring in pediatric intensive care environment","year":2018,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Université TÉLUQ; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Respiratory monitoring; Intensive care; Respiratory system; Intensive care medicine; Respiratory care; Computer science; Medicine; Medical physics; Medical emergency; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003195209,0.0002633727,0.0003153383,0.0003339598,0.0001396149,0.00007082796,0.0002198443,0.00008941076,0.000001686343],"category_scores_gemma":[0.000120313,0.0002755571,0.00006946152,0.0002348839,0.0001925523,0.0001488188,0.0001589902,0.0003394614,0.000005028041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001460983,"about_ca_system_score_gemma":0.00003271613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001109184,"about_ca_topic_score_gemma":0.000001089325,"domain_scores_codex":[0.9983677,0.0000460013,0.0003864282,0.000382968,0.0003357916,0.0004811242],"domain_scores_gemma":[0.9990605,0.0002129624,0.00004919269,0.0002330986,0.0001654021,0.0002788889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005911867,0.00003204377,0.8267822,0.002733848,0.00007059949,0.0004364551,0.004492014,0.0001417678,0.01723973,0.0002023775,0.0005699228,0.14724],"study_design_scores_gemma":[0.03474807,0.0008302592,0.2380153,0.01340607,0.000828406,0.0007054743,0.03040377,0.5547589,0.07573894,0.001318631,0.04070435,0.00854183],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7356392,0.01499212,0.2419365,0.00012833,0.005889614,0.0005692309,0.00001295657,0.0006671745,0.0001649666],"genre_scores_gemma":[0.9926741,0.0003779708,0.004248976,0.0001337954,0.002440085,0.00005823492,0.000004422102,0.00006166172,8.1473e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5887669,"threshold_uncertainty_score":0.9999697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212803748774616,"score_gpt":0.2393271674921282,"score_spread":0.227199130004382,"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."}}