{"id":"W4393867598","doi":"10.20944/preprints202404.0006.v1","title":"Machine Learning for Evaluating Hospital Mobility: An Italian Case Study","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Healthcare Systems and Public Health","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Education and Early Childhood Development","funders":"","keywords":"Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039073,0.0006276856,0.0004046718,0.002004569,0.0009804841,0.001764263,0.001226028,0.001593729,0.001575041],"category_scores_gemma":[0.009435862,0.000169316,0.0008039928,0.004091853,0.001210561,0.001023294,0.00136128,0.0008372287,0.0003046696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004795649,"about_ca_system_score_gemma":0.001404978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02757969,"about_ca_topic_score_gemma":0.02504872,"domain_scores_codex":[0.9969132,0.002107015,0.0001407582,0.0001834709,0.0003174304,0.0003381873],"domain_scores_gemma":[0.9958605,0.002833807,0.0004125965,0.0002852541,0.0003623416,0.0002455342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00134123,0.002807795,0.4254519,0.001302753,0.0003769649,0.03373101,0.02045387,0.2069916,0.002707503,0.05543374,0.02603136,0.2233703],"study_design_scores_gemma":[0.0003387426,0.001724352,0.2892563,0.0009282251,0.0003023602,0.0146083,0.03947526,0.5395489,0.006481833,0.02726536,0.07980745,0.0002628813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9568574,0.001331672,0.01815074,0.004846204,0.00004822616,0.000263035,0.00106579,0.0001042688,0.01733273],"genre_scores_gemma":[0.9866789,0.0006263806,0.01043968,0.0001411879,0.00004621437,0.0001046766,0.0003814966,0.00002004506,0.001561397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02757969,"threshold_uncertainty_score":0.0548383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3038252930107461,"score_gpt":0.5065536018906577,"score_spread":0.2027283088799116,"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."}}