{"id":"W7115920833","doi":"10.2196/78030","title":"Resource Use Patterns in US Telehealth Services: Machine Learning and Clustering Analysis Across 4 Specialties","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Telehealth; Cluster analysis; Resource (disambiguation); Health care; Telemedicine","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":[0.00176948,0.0002984665,0.0003953639,0.003639358,0.0003819762,0.000771075,0.0007056409,0.0005419088,0.001679075],"category_scores_gemma":[0.009652273,0.0001693356,0.0009726848,0.003911487,0.0003524238,0.0006333563,0.0009011904,0.0006581565,0.0004321383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001212655,"about_ca_system_score_gemma":0.000950423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03833263,"about_ca_topic_score_gemma":0.02368056,"domain_scores_codex":[0.9988191,0.0003413759,0.0001571968,0.0002849905,0.0002028925,0.0001944954],"domain_scores_gemma":[0.9946384,0.002506727,0.001339598,0.0004083967,0.0008017346,0.0003051105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009200925,0.0001582693,0.9768562,0.0000319336,0.00009234937,0.00005942825,0.0001995235,0.008628609,0.0001594578,0.0002568736,0.002224383,0.01124104],"study_design_scores_gemma":[0.00001513991,0.00006581363,0.8597112,0.00004153391,0.00003666963,0.0001680015,0.0008419157,0.1367958,0.0003034355,0.0006718412,0.001322819,0.00002586974],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935915,0.0001064499,0.001667252,0.000235069,0.000007767268,0.0000512496,0.003669573,0.00006435095,0.0006067811],"genre_scores_gemma":[0.9907703,0.00007084139,0.002686261,0.00004704275,0.0000105968,0.00008069251,0.006080105,0.00001504945,0.000239061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03833263,"threshold_uncertainty_score":0.07621902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01989092984989333,"score_gpt":0.3722251087267316,"score_spread":0.3523341788768383,"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."}}