{"id":"W2785785876","doi":"10.12927/whp.2017.25306","title":"Accelerating Harmonization in Digital Health","year":2017,"lang":"en","type":"article","venue":"World health & population","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Harmonization; Digital health; Public health; Global health; Environmental health; Political science; Medicine; Health care; Nursing; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006157039,0.0001313962,0.0003528212,0.0003355946,0.0006981867,0.0001011907,0.00007453637,0.0000350617,0.00006218292],"category_scores_gemma":[0.0002486157,0.0001314433,0.00003240545,0.0002250767,0.00001902853,0.000586348,0.00002960506,0.0002227833,0.00001626888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005471191,"about_ca_system_score_gemma":0.0003191842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006464862,"about_ca_topic_score_gemma":0.008808369,"domain_scores_codex":[0.9980783,0.00003975994,0.0008546708,0.0002755524,0.0003063483,0.0004453366],"domain_scores_gemma":[0.9985215,0.0000317325,0.0007591024,0.0003650282,0.00006642909,0.0002561806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003351987,0.00004593686,0.7374935,0.0001969711,0.000002709672,0.000002153701,0.000405745,0.000007121484,0.000003581518,0.001104526,0.0024216,0.2582826],"study_design_scores_gemma":[0.001785739,0.0002253176,0.9896439,0.00029302,0.000003310173,0.000007388585,0.0001271276,0.001270704,0.000006649011,0.0004215514,0.006131454,0.00008378804],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8840364,0.0003679961,0.001577588,0.1095819,0.0005528091,0.00202877,0.00001777494,0.0001534606,0.001683343],"genre_scores_gemma":[0.9891449,0.00006654037,0.002172395,0.006769559,0.0003909337,0.00002892873,0.0009658347,0.00002513635,0.0004357214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2581988,"threshold_uncertainty_score":0.977298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09259850608661495,"score_gpt":0.4263389588586862,"score_spread":0.3337404527720712,"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."}}