{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004730467,0.0006263321,0.0005408373,0.0009886506,0.0002398608,0.0006541571,0.0008649337,0.0008206227,0.008903832],"category_scores_gemma":[0.001238092,0.0003694258,0.0004793406,0.0004282891,0.000134668,0.0004066648,0.000760405,0.0004857046,0.002421284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002315793,"about_ca_system_score_gemma":0.000656284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001136846,"about_ca_topic_score_gemma":0.001256876,"domain_scores_codex":[0.9995783,0.000114192,0.0000397701,0.00008026016,0.00015971,0.00002775712],"domain_scores_gemma":[0.9993431,0.0001926396,0.00005896725,0.00008215589,0.000249787,0.00007330415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001424834,0.0003293513,0.0368925,0.001125056,0.000220366,0.002835645,0.0007702162,0.006950031,0.2381124,0.001890176,0.06026722,0.6491822],"study_design_scores_gemma":[0.0004789849,0.002207804,0.144148,0.0008477528,0.001196717,0.02364823,0.000715691,0.2183772,0.3577532,0.002920476,0.2470751,0.0006309752],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1226192,0.005516078,0.8242686,0.001666562,0.00090417,0.0007863691,0.006651655,0.0237914,0.01379596],"genre_scores_gemma":[0.5817353,0.004037319,0.3895171,0.002844104,0.0004829582,0.001382748,0.003880183,0.001326364,0.01479396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008903832,"threshold_uncertainty_score":0.02978629,"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."}}