{"id":"W2147600954","doi":"10.1258/135763303322196385","title":"Determining clinician satisfaction with telemedicine","year":2003,"lang":"en","type":"article","venue":"Journal of Telemedicine and Telecare","topic":"Patient Satisfaction in Healthcare","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Health Economics","funders":"","keywords":"Ranking (information retrieval); Telemedicine; Preference; Completeness (order theory); Medicine; Patient satisfaction; Scale (ratio); Family medicine; Health care; Nursing; Computer science; Artificial intelligence; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01722156,0.0002689027,0.0007422963,0.001676845,0.0003873904,0.001156495,0.0004185616,0.0006700615,0.004137385],"category_scores_gemma":[0.07295611,0.0002514278,0.0009068362,0.001832027,0.0004522952,0.0009019218,0.0008673721,0.0007323627,0.000636414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001210434,"about_ca_system_score_gemma":0.0008783956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009857223,"about_ca_topic_score_gemma":0.001475956,"domain_scores_codex":[0.9706186,0.01983444,0.003085274,0.0007349867,0.004916843,0.000809876],"domain_scores_gemma":[0.8936654,0.06995887,0.01591099,0.0021469,0.01546227,0.002855661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004493375,0.0007499211,0.8556671,0.0004903054,0.0003225827,0.0001524456,0.003689221,0.000604035,0.001466603,0.0003142334,0.001862917,0.1301872],"study_design_scores_gemma":[0.0006158233,0.008594828,0.9665408,0.0002260903,0.0001962934,0.001067886,0.006085806,0.008194456,0.002630177,0.0005044586,0.005220845,0.0001224965],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991141,0.0005518285,0.003670761,0.0005720364,0.00004642664,0.0003638204,0.0004560859,0.00003826914,0.003159802],"genre_scores_gemma":[0.9939877,0.0002293936,0.004295894,0.0001968901,0.00004495356,0.0004154305,0.0002937223,0.00001180938,0.0005243666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01722156,"threshold_uncertainty_score":0.09107739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06053970602135714,"score_gpt":0.4186147096694152,"score_spread":0.3580750036480581,"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."}}