{"id":"W2592465029","doi":"10.1097/cin.0000000000000336","title":"The Relationship Between Magnet Designation, Electronic Health Record Adoption, and Medicare Meaningful Use Payments","year":2017,"lang":"en","type":"article","venue":"CIN Computers Informatics Nursing","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ambrose University","funders":"National Institute on Minority Health and Health Disparities","keywords":"Incentive; Odds; Electronic health record; Incentive program; Payment; Business; Odds ratio; Health information technology; Receipt; Medicine; Electronic medical record; Medical record; Health care; Logistic regression; Family medicine; Accounting; Finance; Political 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":["sts"],"consensus_categories":[],"category_scores_codex":[0.00441334,0.0002519265,0.0004208075,0.0001586331,0.009791835,0.0002961739,0.000596369,0.0002393338,0.00000700729],"category_scores_gemma":[0.001596078,0.0002070704,0.00004952943,0.0001503917,0.0002235665,0.0009640039,0.0001277942,0.001359229,0.00004818621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001787863,"about_ca_system_score_gemma":0.001543962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002735894,"about_ca_topic_score_gemma":0.0002450078,"domain_scores_codex":[0.9953104,0.0009053978,0.001724044,0.0002127354,0.0005136188,0.001333758],"domain_scores_gemma":[0.9919153,0.004561777,0.001969062,0.0009054283,0.0002728665,0.0003755796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002907174,0.00001694768,0.6657637,0.0006172803,0.00004596518,4.51329e-7,0.02260736,0.00001375529,3.945435e-7,0.0162336,0.04444574,0.2502258],"study_design_scores_gemma":[0.002557188,0.0006330505,0.8636987,0.005979347,0.00006035586,0.00001825367,0.008880934,0.01824393,0.000001583998,0.01193104,0.08743913,0.0005565398],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.425959,0.001799997,0.5168497,0.03613458,0.007859744,0.006198561,0.00003755424,0.0005798873,0.004581006],"genre_scores_gemma":[0.9891104,0.0003839688,0.008075411,0.001263481,0.0005890795,0.0000741293,0.00006835758,0.00004362865,0.0003915765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5631514,"threshold_uncertainty_score":0.9914973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1218197611064921,"score_gpt":0.427935651500026,"score_spread":0.3061158903935339,"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."}}