{"id":"W6968058465","doi":"10.5281/zenodo.1443272","title":"Medical Claims Management Solutions Market Size, Demand, Growth, Segmentation, Analysis and Forecast to 2023","year":2018,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Global Healthcare and Medical Tourism","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Population; Health care; Government (linguistics); Population ageing; Cloud computing; Information technology management; Software deployment; Service provider; Negotiation","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":[0.002137472,0.0005843404,0.0002934103,0.004234377,0.0006553062,0.004514432,0.0008488299,0.0009290011,0.03031435],"category_scores_gemma":[0.008125996,0.0002342064,0.0009559123,0.005590307,0.0003375188,0.003063624,0.0009558835,0.001662349,0.02000619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004669548,"about_ca_system_score_gemma":0.002909817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02296116,"about_ca_topic_score_gemma":0.01451916,"domain_scores_codex":[0.9981735,0.0001077105,0.00008700164,0.0001770767,0.001233999,0.0002208352],"domain_scores_gemma":[0.995029,0.0009624727,0.0007189302,0.0001193984,0.002670881,0.0004992407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005280196,0.0001754817,0.02651892,0.0006029967,0.00004845771,0.0002523806,0.000441959,0.003477898,0.002465136,0.03383342,0.7537305,0.1779248],"study_design_scores_gemma":[0.00007581607,0.0003807339,0.1375141,0.0006492373,0.00007368387,0.0004463337,0.001810258,0.02510531,0.002901887,0.01092175,0.8199878,0.0001330467],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1425903,0.01141344,0.009950283,0.03468185,0.002149992,0.000648813,0.3320317,0.003517592,0.4630159],"genre_scores_gemma":[0.4893274,0.01329565,0.01059768,0.004092431,0.001869816,0.0007645856,0.3043046,0.001196149,0.1745517],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.03031435,"threshold_uncertainty_score":0.1014116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05196690446505825,"score_gpt":0.3646031990667543,"score_spread":0.312636294601696,"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."}}