{"id":"W3102685957","doi":"10.1016/j.resuscitation.2020.10.035","title":"Crowdsourcing to save lives: A scoping review of bystander alert technologies for out-of-hospital cardiac arrest","year":2020,"lang":"en","type":"review","venue":"Resuscitation","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":98,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kingston Health Sciences Centre; McGill University; Queen's University","funders":"","keywords":"Crowdsourcing; Medicine; Mobile phone; Medical emergency; Phone; The Internet; Cardiopulmonary resuscitation; Telemedicine; Bystander effect; Health care; Emergency medicine; World Wide Web; Resuscitation; Computer science","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"],"consensus_categories":[],"category_scores_codex":[0.0006356073,0.0004073564,0.003015611,0.0003645334,0.00006411566,0.00001906949,0.0001818166,0.0002884208,0.00000351435],"category_scores_gemma":[0.00490846,0.000336496,0.001298904,0.0008037688,0.0001093795,0.0000925948,0.00009106204,0.0002599584,0.00001710961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001894029,"about_ca_system_score_gemma":0.0006872424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005035937,"about_ca_topic_score_gemma":0.000002656885,"domain_scores_codex":[0.9971683,0.0001307585,0.001334846,0.0005573258,0.0005207606,0.0002880163],"domain_scores_gemma":[0.9968574,0.001013234,0.0009573041,0.0004863518,0.0005743003,0.000111419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00004097657,0.00003418249,0.00001309551,0.5871649,0.0003980942,0.000005360936,0.0006910746,0.000001500602,0.00002507084,0.0001127906,0.002479407,0.4090336],"study_design_scores_gemma":[0.0002548253,0.0002741108,0.00000995988,0.6851211,0.001893705,3.106349e-7,0.0005965572,0.000003408444,0.00003662961,0.00003472892,0.3114983,0.0002763166],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000006321164,0.9839284,0.002340985,0.0007747017,0.00416003,0.008136291,0.0001574555,0.0001260585,0.0003697407],"genre_scores_gemma":[0.00006425039,0.994355,0.00386736,0.00005218172,0.0004868284,0.0006655474,0.0004134065,0.00007830543,0.00001711256],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.4087573,"threshold_uncertainty_score":0.9999087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04072676108735244,"score_gpt":0.3590206803024958,"score_spread":0.3182939192151434,"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."}}