{"id":"W4328007648","doi":"10.2139/ssrn.4381132","title":"Cost Effective Data Mining Approach for Outpatient Scheduling: Analyzing the Performance of Appointment Scheduling Systems","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Scheduling (production processes); Computer science; Operations research; Operations management; Economics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01306896,0.0001917086,0.000368058,0.0002320265,0.002347315,0.00004911752,0.0005774277,0.0001732477,0.000003781847],"category_scores_gemma":[0.0007529536,0.0001342841,0.00008208091,0.000684978,0.00004517764,0.0002885778,0.0001682646,0.002003587,0.00001229193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009302071,"about_ca_system_score_gemma":0.003290893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007896758,"about_ca_topic_score_gemma":0.0000669354,"domain_scores_codex":[0.9956177,0.0007308527,0.0009757034,0.0003607168,0.0003349111,0.001980054],"domain_scores_gemma":[0.9973714,0.0007439989,0.0006312161,0.0005778662,0.0005702816,0.000105252],"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":[0.0002729882,0.0001341731,0.03044956,0.0009551923,0.0004662396,4.883706e-7,0.006375893,0.9105681,0.0001501466,0.01478732,0.0001437953,0.03569613],"study_design_scores_gemma":[0.0009253206,0.0004143982,0.0003710072,0.0003479071,0.00007558247,0.0000106997,0.03581822,0.9613985,0.0000127245,0.000102287,0.0003778848,0.0001454498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5739148,0.001684999,0.4192951,0.000627614,0.000612696,0.003677043,0.00003469778,0.00006059356,0.00009242506],"genre_scores_gemma":[0.9819202,0.002705969,0.01325281,0.00005095922,0.0006008822,0.0007849556,0.0003415949,0.00004777107,0.0002948236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4080054,"threshold_uncertainty_score":0.9989515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1120194730257786,"score_gpt":0.4081825662833064,"score_spread":0.2961630932575278,"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."}}