{"id":"W2061738585","doi":"10.1371/journal.pone.0032692","title":"Prehospital Electronic Patient Care Report Systems: Early Experiences from Emergency Medical Services Agency Leaders","year":2012,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Gillings School of Public Health; Robert Wood Johnson Foundation; American College of Emergency Physicians; U.S. Department of Veterans Affairs","keywords":"Snowball sampling; Agency (philosophy); Information system; Emergency medical services; Medical emergency; Medicine; Health care; Leverage (statistics); Business; Nursing; Computer science; Engineering; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01223737,0.000575447,0.0005477987,0.001010707,0.007811482,0.00611877,0.001958697,0.002646495,0.002438166],"category_scores_gemma":[0.03704062,0.0009905801,0.0004720454,0.000963064,0.004095702,0.005452747,0.006265863,0.004551911,0.0006430881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003077644,"about_ca_system_score_gemma":0.005986343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005829033,"about_ca_topic_score_gemma":0.01036855,"domain_scores_codex":[0.9862277,0.009260978,0.0004812183,0.0005406744,0.001220934,0.002268545],"domain_scores_gemma":[0.9625735,0.01934603,0.004122373,0.0008345805,0.00616917,0.006954253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004542461,0.0001457716,0.008476937,0.0001251666,0.000006266697,0.001847929,0.9802355,0.00002396251,0.0005794581,0.0003448724,0.001784931,0.006383883],"study_design_scores_gemma":[0.000005645257,0.0001257902,0.002255358,0.00008898781,0.000003952154,0.000323843,0.9893683,0.00006139811,0.0001759205,0.0000568665,0.007521993,0.00001195324],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877265,0.0005625038,0.001137702,0.006405275,0.0001232371,0.0001523115,0.00006020129,0.00002985386,0.003802334],"genre_scores_gemma":[0.9936633,0.0008685025,0.0009047013,0.001991901,0.00007039967,0.0001510619,0.00006941386,0.00003493792,0.002245754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01223737,"threshold_uncertainty_score":0.06471819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06759092281436667,"score_gpt":0.3660542452769703,"score_spread":0.2984633224626036,"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."}}