{"id":"W4411111569","doi":"10.2196/70040","title":"Analyzing Disparity in Geographical Accessibility to Home Medical Care Using a Claims Database and Geographical Information System: Simulation Study","year":2025,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Global Health Workforce Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Database; Computer science; Geography; Information retrieval; Data science; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002194036,0.0002255476,0.000494894,0.0008906814,0.001031268,0.00006665358,0.0002715227,0.0003136328,0.00003393539],"category_scores_gemma":[0.0004280214,0.0002098884,0.00005084015,0.002209979,0.00007334738,0.0007355379,0.0006723411,0.00107868,0.000007333285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005188948,"about_ca_system_score_gemma":0.0002896726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004046068,"about_ca_topic_score_gemma":0.003347288,"domain_scores_codex":[0.9960058,0.0009670701,0.001238205,0.000489442,0.0005976767,0.0007017567],"domain_scores_gemma":[0.998026,0.0006258954,0.0001950979,0.0005059196,0.000255246,0.0003918763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001129481,0.00007692091,0.9892019,0.002025843,0.00001213221,0.000007131853,0.004862186,0.001040715,0.000001116138,0.0003084414,0.00002178606,0.002328888],"study_design_scores_gemma":[0.001268443,0.00003583748,0.8827996,0.003342102,0.00003291413,6.13399e-7,0.02708109,0.08501171,3.100307e-7,0.00004144473,0.0002168548,0.0001690947],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890438,0.000191399,0.006311913,0.0004456919,0.0003693374,0.003189072,0.00002465228,0.0001861263,0.0002379821],"genre_scores_gemma":[0.9987175,0.000007596414,0.000365553,0.0005124946,0.00008015161,0.0002429675,0.00005993958,0.00001019105,0.000003608098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1064023,"threshold_uncertainty_score":0.8559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03580726718499317,"score_gpt":0.465014493597903,"score_spread":0.4292072264129099,"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."}}