{"id":"W2947711622","doi":"10.2196/12135","title":"A Mobile Phone App for the Self-Management of Pediatric Concussion: Development and Usability Testing","year":2019,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Holland Bloorview Kids Rehabilitation Hospital; Toronto Rehabilitation Institute; University of Toronto","funders":"Ontario Neurotrauma Foundation; Canadian Institutes of Health Research; Government of Ontario; Bloorview Research Institute; Ontario Brain Institute","keywords":"Usability; Concussion; Mobile phone; Smart phone; Phone; Mobile apps; Computer science; Internet privacy; Psychology; Applied psychology; World Wide Web; Medicine; Medical emergency; Injury prevention; Human–computer interaction; Poison control; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005045109,0.000130862,0.0002489046,0.00008162809,0.0001400785,0.00001746738,0.0001385421,0.00005294074,0.0001774124],"category_scores_gemma":[0.00004743434,0.00007767331,0.00004910492,0.0002303478,0.00006648707,0.00004169329,0.0001229206,0.0001356032,0.00001783158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008261909,"about_ca_system_score_gemma":0.00008021747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006523962,"about_ca_topic_score_gemma":0.000001089118,"domain_scores_codex":[0.9987509,0.00002608832,0.0003366483,0.0002539629,0.0003912936,0.0002411184],"domain_scores_gemma":[0.9987811,0.0005666578,0.0001041449,0.0003512421,0.0001039495,0.00009291081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001385059,0.0006303554,0.9576202,0.005095839,0.0003393051,0.000003023113,0.01010393,0.000003460681,0.001651916,0.0003360955,0.001529661,0.02254771],"study_design_scores_gemma":[0.002081787,0.000556363,0.9878094,0.00008572447,0.00008419195,0.000002133796,0.002549923,0.00009097801,0.001829732,0.00005014655,0.004725799,0.0001338524],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99531,0.00009239769,0.00002751105,0.00002855584,0.0000588681,0.00321301,0.000005161357,0.00006609907,0.001198384],"genre_scores_gemma":[0.9951677,0.00000491155,0.00330305,0.00001490325,0.00005220611,0.0004075993,0.000008058152,0.00001767807,0.001023858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03018917,"threshold_uncertainty_score":0.3167426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0762754548740266,"score_gpt":0.3552996423874097,"score_spread":0.2790241875133831,"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."}}