{"id":"W4401941807","doi":"10.2139/ssrn.4938151","title":"Outcomes of a Telemedicine Health Assistance System During COVID-19 Pandemic","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; Impact","funders":"","keywords":"Telemedicine; Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Medical emergency; Virology; Health care; Economic growth; Economics; Internal medicine; Outbreak","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002652409,0.0004580183,0.0004598077,0.0008403211,0.0015962,0.002265517,0.0009658129,0.001790498,0.006919926],"category_scores_gemma":[0.01927657,0.000327392,0.001129858,0.001225451,0.0009246467,0.002295202,0.00304949,0.002748887,0.0007669415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004835437,"about_ca_system_score_gemma":0.00456826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07873684,"about_ca_topic_score_gemma":0.05722858,"domain_scores_codex":[0.9957049,0.001383691,0.0003658572,0.0003740873,0.0004309733,0.001740512],"domain_scores_gemma":[0.987417,0.002774942,0.003374419,0.0004213617,0.002654109,0.003358229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005982285,0.004507355,0.951238,0.0002681282,0.0004544056,0.001026544,0.004323633,0.003223874,0.00054044,0.0008700973,0.006779227,0.02078613],"study_design_scores_gemma":[0.0001728444,0.002689078,0.9859635,0.00009621026,0.0001330758,0.0001159205,0.007870972,0.001559316,0.000265986,0.0002620911,0.0008171098,0.0000538947],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942818,0.00007014978,0.0000502387,0.00107655,0.0000375401,0.0001314734,0.002058943,0.00001119761,0.002282187],"genre_scores_gemma":[0.9982423,0.00003963272,0.00003438336,0.0001670587,0.00002025325,0.0000587115,0.0009295057,0.000004393798,0.0005037993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07873684,"threshold_uncertainty_score":0.1565571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0391468210360156,"score_gpt":0.3903678453516943,"score_spread":0.3512210243156787,"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."}}