{"id":"W2624962964","doi":"10.21785/icad2017.066","title":"Did You Feel That? Developing Novel Multimodal Alarms for High Consequence Clinical Environments","year":2017,"lang":"en","type":"article","venue":"","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"ALARM; Perception; Haptic technology; Computer science; Human–computer interaction; Audiology; Alarm signal; Speech recognition; Psychology; Medicine; Simulation; Engineering; Neuroscience","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":[],"consensus_categories":[],"category_scores_codex":[0.0003720782,0.0001366827,0.0003001793,0.00004140845,0.0004449667,0.00001571621,0.0002230345,0.0003403573,0.00001867095],"category_scores_gemma":[0.0008441349,0.0001134536,0.00005992056,0.00001599533,0.0002515627,0.0001034228,0.0001102413,0.0003073533,0.00003791843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000740362,"about_ca_system_score_gemma":0.000105624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003053986,"about_ca_topic_score_gemma":0.00002645073,"domain_scores_codex":[0.9987932,0.00001547916,0.0003519214,0.0003552608,0.000153628,0.0003304788],"domain_scores_gemma":[0.998817,0.0002151437,0.0001875729,0.0006153747,0.00002908045,0.0001358022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002021504,0.000216969,0.9364352,0.00007973047,0.00008516605,0.00004577646,0.00007264147,5.078927e-7,0.001854266,0.002966331,0.0001390661,0.05790225],"study_design_scores_gemma":[0.00469947,0.0006229308,0.9639434,0.0002880864,0.00005305791,0.00005965723,0.0001443641,0.0002632873,0.01878477,0.0002941635,0.01058443,0.0002623594],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9683489,0.00002512331,0.02303691,0.006445698,0.001076425,0.0006370318,0.00001120551,0.0001042394,0.0003144288],"genre_scores_gemma":[0.9421917,0.00007137246,0.05576514,0.0007509177,0.0002118561,0.00006176189,0.00001410813,0.00001721673,0.0009159166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0576399,"threshold_uncertainty_score":0.4626502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2320897653643851,"score_gpt":0.4399780014474173,"score_spread":0.2078882360830322,"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."}}