{"id":"W4283522217","doi":"10.2196/39181","title":"The Association Between Telehealth Utilization and Policy Responses on COVID-19 in Japan: Interrupted Time-Series Analysis","year":2022,"lang":"en","type":"article","venue":"Interactive Journal of Medical Research","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Telehealth; Coronavirus disease 2019 (COVID-19); Pandemic; Medicine; Medical prescription; Telemedicine; Family medicine; Medical emergency; Business; Nursing; Health care; Political science; Disease; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005618038,0.0003376002,0.0006352058,0.001539851,0.0003009794,0.001188772,0.0007753337,0.0006328208,0.00158176],"category_scores_gemma":[0.01747721,0.0002611358,0.001275371,0.002683071,0.0003983405,0.000722289,0.0009473126,0.001179195,0.0001933779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431296,"about_ca_system_score_gemma":0.001076373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02820727,"about_ca_topic_score_gemma":0.01049336,"domain_scores_codex":[0.9962508,0.001295954,0.0007266239,0.0006066953,0.0005619778,0.0005579414],"domain_scores_gemma":[0.9815916,0.005833702,0.008777858,0.0009810687,0.001603878,0.001211844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002940761,0.00008109165,0.9956883,0.00004079532,0.0002057412,0.00007481386,0.0002909093,0.0006576717,0.00008166487,0.00009253665,0.0002855126,0.002206909],"study_design_scores_gemma":[0.00001178759,0.00009794591,0.9934254,0.00001790193,0.00008719985,0.00004430133,0.0004062664,0.005495353,0.00005998899,0.000048349,0.0002951638,0.00001041835],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972087,0.0003196059,0.0006085065,0.0001573171,0.00001572195,0.00003164427,0.001308136,0.00001081771,0.0003396891],"genre_scores_gemma":[0.9970183,0.0001516009,0.0002696975,0.00003966688,0.00001929467,0.0000552825,0.002250682,0.000005080611,0.0001904554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02820727,"threshold_uncertainty_score":0.05608618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1555691884510341,"score_gpt":0.5578393102516013,"score_spread":0.4022701218005672,"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."}}