{"id":"W4413401094","doi":"10.1080/01605682.2025.2546058","title":"Patient segmentation and resource allocation for tailored healthcare delivery","year":2025,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Blood Services","keywords":"Health care; Computer science; Healthcare delivery; Purchasing; Resource allocation; Project management; Segmentation; Business; Operations management; Knowledge management; Operations research; Artificial intelligence; Marketing; Engineering; Systems engineering; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006301615,0.0000459891,0.00006432481,0.00003573486,0.0003359966,0.00005470168,0.00008605408,0.00003033635,0.000002904807],"category_scores_gemma":[0.0001196136,0.00003379011,0.00006753787,0.000155484,0.00004663119,0.0001987807,0.00003252966,0.0002461115,2.895059e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002104406,"about_ca_system_score_gemma":0.0001252076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002973253,"about_ca_topic_score_gemma":0.000002793067,"domain_scores_codex":[0.9992604,0.00006194766,0.0001926604,0.00005506867,0.0003114182,0.0001185272],"domain_scores_gemma":[0.9991962,0.0002007963,0.0000318958,0.00006140977,0.0004751812,0.0000345328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002919926,0.000118291,0.002069806,0.0007005041,0.0004445052,0.000001339918,0.01033392,0.4752538,0.1657487,0.01343195,0.2114749,0.1201304],"study_design_scores_gemma":[0.004248295,0.0005848348,0.01301661,0.001153686,0.00007901518,0.00004483336,0.03320177,0.6059468,0.1270374,0.02159989,0.1926323,0.0004544905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8448561,0.005915747,0.09996294,0.04714538,0.0005305635,0.001074246,0.00002590846,0.00002753502,0.0004615234],"genre_scores_gemma":[0.9906321,0.0003695805,0.008015882,0.0006272491,0.0001033366,0.00002205625,0.000005937894,0.000007377578,0.0002165289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1457759,"threshold_uncertainty_score":0.2584248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0265614889590065,"score_gpt":0.3436137566803511,"score_spread":0.3170522677213446,"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."}}