{"id":"W2746680482","doi":"10.1073/pnas.1700618114","title":"Distribution of lifetime nursing home use and of out‐of‐pocket spending","year":2017,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"National Institute on Aging","keywords":"Percentile; Nursing homes; Population; Demography; Medicine; Demographic economics; Distribution (mathematics); Matching (statistics); Health and Retirement Study; Long-term care; Cohort; Health care; Current Population Survey; Health insurance; Gerontology; Environmental health; Nursing; Economics; Statistics; Economic growth","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":[0.001151482,0.000247173,0.0003653386,0.002228688,0.0001944691,0.0007238815,0.000529176,0.0005273982,0.005280552],"category_scores_gemma":[0.006312402,0.0002043835,0.0005100296,0.001359171,0.0003658963,0.0007780838,0.0008223527,0.0004061298,0.0009293779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003782531,"about_ca_system_score_gemma":0.0001673726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00603571,"about_ca_topic_score_gemma":0.003033545,"domain_scores_codex":[0.9992441,0.0001979705,0.00009413966,0.000206383,0.0001126948,0.0001447936],"domain_scores_gemma":[0.9951105,0.002196489,0.001234589,0.0005085694,0.0005944288,0.0003554792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003501705,0.00007630054,0.9865177,0.0000174574,0.00009210257,0.0001154832,0.000372013,0.002585727,0.0005365675,0.0006568688,0.0006610844,0.008018642],"study_design_scores_gemma":[0.000009645712,0.0001051396,0.9853689,0.00001435689,0.00001626175,0.0004035564,0.0004833239,0.01195665,0.0001665906,0.0006599344,0.0007905377,0.00002507559],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942708,0.0001221789,0.0005887469,0.00004870604,0.000003161355,0.0000136645,0.003959657,0.00001471663,0.0009783752],"genre_scores_gemma":[0.9946174,0.0001227745,0.0001876461,0.00001239107,0.000008346441,0.00002767125,0.004138527,0.000009028425,0.0008762633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00603571,"threshold_uncertainty_score":0.01766521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1183257555907613,"score_gpt":0.4284881627000281,"score_spread":0.3101624071092668,"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."}}