{"id":"W3032973540","doi":"10.2196/17352","title":"Differences in Electronic Personal Health Information Tool Use Between Rural and Urban Cancer Patients in the United States: Secondary Data Analysis","year":2020,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Cancer survivorship and care","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Health Information National Trends Survey; Cancer; Medicine; Rural area; Logistic regression; Health care; Family medicine; Demography; Environmental health; Gerontology; Health information; Internal medicine; Economic growth; Pathology","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":[0.0001497268,0.000128083,0.0003147124,0.0002102103,0.00004459984,0.00005887834,0.0001569185,0.00004706809,0.0001501687],"category_scores_gemma":[0.00001859098,0.00009157046,0.00003466052,0.001266169,0.00003736778,0.0004744325,0.00005797643,0.0003604481,0.000001089397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002855115,"about_ca_system_score_gemma":0.0004326492,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05172805,"about_ca_topic_score_gemma":0.08049161,"domain_scores_codex":[0.998841,0.00009685269,0.0002946583,0.0001879807,0.0002927177,0.0002867652],"domain_scores_gemma":[0.9995058,0.00006380806,0.0001094797,0.0001856268,0.00005092031,0.00008434148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001309549,0.00001830361,0.9525541,0.0001074355,0.0001117301,5.684478e-7,0.02863124,0.000006901064,3.393211e-7,0.000004650956,0.001903985,0.0165298],"study_design_scores_gemma":[0.00102009,0.0001647478,0.9704938,0.00007196752,0.0001009899,1.308814e-7,0.003647008,0.003031921,9.866517e-7,0.000003400158,0.02135901,0.0001059448],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926649,0.001061005,0.00001640963,0.004467379,0.00003538119,0.0004743868,0.001253229,0.00001409882,0.00001325482],"genre_scores_gemma":[0.9859126,0.001750581,0.000002068966,0.008403065,0.00009381281,0.0001297291,0.003689593,0.000006526652,0.00001208098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02876355,"threshold_uncertainty_score":0.9545866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.038821411842043,"score_gpt":0.3136200161994961,"score_spread":0.2747986043574531,"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."}}