{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":11,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":11,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"c6c61459746e","filters":{"venue":"Operations Research for Health Care"}},"results":[{"id":"W1970872931","doi":"10.1016/j.orhc.2013.03.001","title":"Multi-criteria decision analysis (MCDA) in health care: A bibliometric analysis","year":2013,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":178,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Programs for Assessment of Technology in Health Research Institute; McMaster University","funders":"Pfizer Canada; Pfizer","keywords":"Multiple-criteria decision analysis; Health care; Decision analysis; Transparency (behavior); Management science; Operations research; Computer science; Medicine; Political science; Economics; Engineering; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.3606274506282388,"gpt":0.6206862944118692,"spread":0.2600588437836304,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.02102559,0.0004086442,0.001729502,0.27053,0.002477856,0.003933222,0.002288337,0.0002559623,0.002898894],"category_scores_gemma":[0.02402073,0.0003337881,0.0008431994,0.4553356,0.0001937197,0.001408422,0.0006789419,0.000744812,0.0006403365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002112691,"about_ca_system_score_gemma":0.002422314,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0193444,"about_ca_topic_score_gemma":0.095598,"domain_scores_codex":[0.9824317,0.003635696,0.003605722,0.002160147,0.006195838,0.001970904],"domain_scores_gemma":[0.9789537,0.006621515,0.0003327477,0.002871734,0.009945201,0.001275153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000201079,0.0005228431,0.04111974,0.0002008618,0.000606948,0.00001209669,0.0229448,0.07281745,0.0002089098,0.000253484,0.04926787,0.8118439],"study_design_scores_gemma":[0.002016878,0.0005473841,0.3441489,0.00007657146,0.00007676705,0.000001946955,0.02232168,0.5940195,0.00001719197,0.0001648442,0.0361525,0.0004557703],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5562984,0.01443185,0.399214,0.01791862,0.0007876722,0.009191401,0.001748314,0.0001428364,0.0002668989],"genre_scores_gemma":[0.842609,0.0003491373,0.1536921,0.001027583,0.0001043792,0.001135956,0.0004378073,0.00004400617,0.0005999487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8113881,"threshold_uncertainty_score":0.9999114,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2761087938","doi":"10.1016/j.orhc.2017.08.006","title":"Blood inventory management in hospitals: Considering supply and demand uncertainty and blood transshipment possibility","year":2017,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Blood donation and transfusion practices","field":"Business, Management and Accounting","cited_by":100,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Iran National Science Foundation","keywords":"Blood management; Economic shortage; Inventory management; Blood supply; Computer science; Operations management; Expiration date; Blood units; Supply chain; Operations research; Blood transfusion; Business; Medicine; Economics; Marketing; Mathematics; Surgery","retraction":null,"screen_n_in":null,"score":{"opus":0.06286189839080315,"gpt":0.3947615173652853,"spread":0.3318996189744822,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002114179,0.0001262452,0.0001848189,0.0003153725,0.0022209,0.001170275,0.0001804841,0.00005583756,0.00004756723],"category_scores_gemma":[0.0002779696,0.0001212031,0.00002951966,0.0001291131,0.0001826726,0.00125163,0.0001440603,0.0002274541,0.000003620319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004117319,"about_ca_system_score_gemma":0.0001123677,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004178331,"about_ca_topic_score_gemma":0.02936884,"domain_scores_codex":[0.9984537,0.00006988735,0.0003230153,0.0004353627,0.0003345158,0.0003835438],"domain_scores_gemma":[0.9991393,0.00008923349,0.00005854248,0.000338587,0.0003144382,0.00005986546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006366813,0.001934594,0.5166336,0.01551534,0.0005851719,0.0001273766,0.01488509,0.000902356,0.0003771873,0.2719493,0.002283577,0.1741697],"study_design_scores_gemma":[0.03223356,0.0007621152,0.7602858,0.001391272,0.0004638077,0.00002373053,0.04980219,0.01977301,0.0003962624,0.008679911,0.1246637,0.001524615],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9710538,0.002093975,0.00006072906,0.02201133,0.0000944094,0.002109179,0.00002796013,0.00002826977,0.002520314],"genre_scores_gemma":[0.996568,0.001029375,0.001027209,0.0007422729,0.00009024581,0.0003623699,0.00004636473,0.0000151459,0.0001190656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2632694,"threshold_uncertainty_score":0.9998666,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3049198493","doi":"10.1016/j.orhc.2021.100290","title":"A decision integration strategy for short-term demand forecasting and ordering for red blood cell components","year":2021,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Blood donation and transfusion practices","field":"Business, Management and Accounting","cited_by":48,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Blood Services; McMaster University; University of Calgary","funders":"Mitacs; Canadian Blood Services","keywords":"Economic shortage; Supply chain; Demand forecasting; Key (lock); Inventory management; Supply and demand; Blood management; Supply chain management","retraction":null,"screen_n_in":null,"score":{"opus":0.1697736990848324,"gpt":0.4172118591312488,"spread":0.2474381600464164,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001166733,0.0001234614,0.0001848742,0.0002669839,0.001953113,0.0009201464,0.0001057035,0.00007452854,0.00003047055],"category_scores_gemma":[0.0007779816,0.000118944,0.00006444588,0.0003454488,0.00002772543,0.001023425,0.00006206598,0.0001560757,0.000001766028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004271312,"about_ca_system_score_gemma":0.0002424794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001563409,"about_ca_topic_score_gemma":0.006184783,"domain_scores_codex":[0.9985163,0.00003881344,0.0003823982,0.0003946913,0.0002896378,0.0003781864],"domain_scores_gemma":[0.9970442,0.0005212754,0.00004911934,0.0001578313,0.002181713,0.00004585092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002149306,0.001665167,0.00406107,0.01403345,0.0002303618,0.00001636871,0.003980685,0.009543025,0.03775484,0.06711914,0.01421281,0.8452338],"study_design_scores_gemma":[0.01343688,0.0008471507,0.003338913,0.0009321839,0.0002234881,0.00002080695,0.01787825,0.7546275,0.00691421,0.003032184,0.1979196,0.0008288429],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7048651,0.002522581,0.2687809,0.01299824,0.0005728579,0.008012348,0.0002867713,0.0001124425,0.001848818],"genre_scores_gemma":[0.9738942,0.0001541743,0.02268402,0.0004327149,0.0003481844,0.0009635225,0.001293074,0.00003326689,0.0001968618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8444049,"threshold_uncertainty_score":0.9993462,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1971698939","doi":"10.1016/j.orhc.2013.12.003","title":"A simulation model for perioperative process improvement","year":2014,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":45,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"University Health Network; University of Toronto; University of New Brunswick","funders":"","keywords":"Overtime; Perioperative; Operations management; Schedule; Scheduling (production processes); Medicine; Revenue; Surgical procedures; Operations research; Medical emergency; Computer science; Surgery; Engineering; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.246544303611805,"gpt":0.6188538996496851,"spread":0.3723095960378801,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004194641,0.0002030304,0.0003539673,0.0003108857,0.009714887,0.0001147836,0.000220256,0.0002462413,0.00005753124],"category_scores_gemma":[0.003746795,0.0001838863,0.00008546648,0.0004448399,0.00007037987,0.0003714208,0.00004988888,0.0006069646,0.00003156225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001047034,"about_ca_system_score_gemma":0.005365281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000312235,"about_ca_topic_score_gemma":0.004728294,"domain_scores_codex":[0.9959777,0.0008120698,0.0009442343,0.0006145911,0.000526356,0.001125067],"domain_scores_gemma":[0.9900981,0.001238284,0.0000839865,0.0004649558,0.007727495,0.0003872319],"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":[0.0001023537,0.00005057238,0.00004790647,0.001017501,0.000009051737,2.185524e-8,0.03414533,0.9391481,0.00006308255,0.01659035,0.001228466,0.00759728],"study_design_scores_gemma":[0.001475593,0.0009465793,0.00002115847,0.0001354358,0.000005130182,6.588184e-8,0.01186025,0.9661746,0.00002138024,0.0001966441,0.01899251,0.0001706138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01687707,0.0001317317,0.9463406,0.02160327,0.0002842738,0.01346464,0.0005612054,0.0001089355,0.0006282642],"genre_scores_gemma":[0.9023089,0.00003441717,0.07150548,0.003944574,0.0006037055,0.01652182,0.001579263,0.00007535628,0.003426456],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8854319,"threshold_uncertainty_score":0.9915743,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3096592410","doi":"10.1016/j.orhc.2020.100276","title":"Robust combined operating room planning and personnel scheduling under uncertainty","year":2020,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"National Science Foundation","keywords":"Overtime; Staffing; Operations management; Scheduling (production processes); Computer science; Robust optimization; Operations research; Robustness (evolution); Schedule; Medicine; Engineering; Economics; Mathematical optimization; Nursing","retraction":null,"screen_n_in":null,"score":{"opus":0.3490122209537824,"gpt":0.5277251187666284,"spread":0.178712897812846,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002600682,0.0002343361,0.0004297771,0.00022414,0.01227122,0.0002257606,0.000222799,0.0002727614,0.0001343999],"category_scores_gemma":[0.002302098,0.0002220153,0.00005881627,0.0007374753,0.00009133446,0.000379226,0.0001637083,0.001487813,0.00004199532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006558793,"about_ca_system_score_gemma":0.003884825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001484403,"about_ca_topic_score_gemma":0.001710412,"domain_scores_codex":[0.9951723,0.00151972,0.0009037188,0.0007139906,0.0005252766,0.001165031],"domain_scores_gemma":[0.9956592,0.0008163357,0.00007374318,0.0003153026,0.002286357,0.0008490867],"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":[0.0000847661,0.00002535186,0.001358169,0.0008833326,0.00001977497,0.000001379306,0.07855351,0.9081526,0.0001272388,0.007548999,0.002321562,0.00092325],"study_design_scores_gemma":[0.00150612,0.0006545022,0.0003441927,0.0003581338,0.000006032783,0.000001037105,0.1962847,0.7966502,0.000006227164,0.00001611095,0.003960539,0.0002122448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.409524,0.004719629,0.3048948,0.2676433,0.0006149479,0.01059357,0.0005314024,0.0004013914,0.001076934],"genre_scores_gemma":[0.8939484,0.0001776057,0.09130301,0.01125746,0.0007562403,0.001223282,0.000936387,0.00007867984,0.0003189871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4844243,"threshold_uncertainty_score":0.9890147,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2123955731","doi":"10.1016/j.orhc.2015.02.001","title":"Modeling and simulation of a hospital evacuation before a forecasted flood","year":2015,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Santé","keywords":"Flood myth; Computer science; Emergency evacuation; Process (computing); Plan (archaeology); Operations research; Emergency plan; Operations management; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.0874630033583757,"gpt":0.427039281241799,"spread":0.3395762778834233,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005621695,0.00006761007,0.0001105829,0.0001760371,0.0001878356,0.00004758884,0.00005482651,0.00005915665,0.000002914655],"category_scores_gemma":[0.0004118717,0.00006900147,0.0000217722,0.0002393537,0.00002181674,0.0002133728,0.00001803658,0.0001121008,0.000002112598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000236401,"about_ca_system_score_gemma":0.0003593377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009969217,"about_ca_topic_score_gemma":0.001660988,"domain_scores_codex":[0.9990776,0.00005004663,0.0002484052,0.0001184462,0.0002955886,0.0002099227],"domain_scores_gemma":[0.9983942,0.00003585211,0.000008747363,0.0001313716,0.001243846,0.0001860211],"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":[0.000009130366,0.00001117591,0.00004231633,0.0001445395,0.000006800911,5.297366e-8,0.008693963,0.9840334,0.00001215188,0.000740647,0.0001386303,0.006167204],"study_design_scores_gemma":[0.0005163995,0.0004089581,0.00003771835,0.00003037972,0.00000277318,2.689191e-7,0.004001256,0.9945164,0.00001654018,0.0001695645,0.0002447108,0.00005505747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7131445,0.0007576126,0.2832589,0.001006466,0.000118837,0.001331544,0.0001604323,0.00007079477,0.0001509081],"genre_scores_gemma":[0.9938742,0.00002743493,0.005426675,0.00002299294,0.00005577517,0.0001403619,0.0004048829,0.00002108029,0.00002661976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2807297,"threshold_uncertainty_score":0.2813798,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2797898861","doi":"10.1016/j.orhc.2018.03.007","title":"A case study of nonlinear programming approach for repeated testing of HIV in a population stratified by subpopulations according to different risks of new infections","year":2018,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"HIV Research and Treatment","field":"Immunology and Microbiology","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Waterloo; Public Health Agency of Canada","funders":"Public Health Agency of Canada","keywords":"Population; Human immunodeficiency virus (HIV); Medicine; Demography; Repeated measures design; Statistics; Mathematics; Environmental health; Immunology","retraction":null,"screen_n_in":null,"score":{"opus":0.2412928337184465,"gpt":0.4934283727285815,"spread":0.252135539010135,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006896298,0.0001099545,0.0003338376,0.0005229357,0.0006845455,0.0000192285,0.0001035347,0.00008831593,0.000007051676],"category_scores_gemma":[0.00110738,0.00009696419,0.00004995622,0.0008180214,0.00007553336,0.00007718652,0.00005524981,0.0001871533,0.000001157276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002080652,"about_ca_system_score_gemma":0.0004787006,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05377364,"about_ca_topic_score_gemma":0.04626204,"domain_scores_codex":[0.9981173,0.0003783203,0.0006644635,0.0003021647,0.0001117169,0.0004260345],"domain_scores_gemma":[0.9979977,0.0003458672,0.00008787757,0.0002856859,0.001199894,0.00008299514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00148133,0.009724154,0.7571038,0.001501071,0.0005258494,0.000006610685,0.07133514,0.009557809,0.03858925,0.0002713535,0.001613759,0.1082899],"study_design_scores_gemma":[0.04914302,0.1314327,0.133682,0.001257765,0.0003062483,0.0002698106,0.5830742,0.04723214,0.05012172,0.00006871916,0.002074681,0.00133702],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869805,0.0001395217,0.005161403,0.0001164881,0.00002895247,0.006690203,0.0008546824,0.00001430979,0.0000139678],"genre_scores_gemma":[0.9854975,0.000002652812,0.01099106,0.000002596785,0.00002326083,0.00123258,0.002144004,0.00001561492,0.00009071988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6234218,"threshold_uncertainty_score":0.9711412,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388872015","doi":"10.1016/j.orhc.2023.100411","title":"Surgical scheduling to smooth demand for resources","year":2023,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Overcrowding; Schedule; Operations management; Integer programming; Scheduling (production processes); Elective surgery; Operations research; Hospital bed; Computer science; Smoothing; Solver; On demand; Medicine; Economics; Engineering; Surgery; Nursing","retraction":null,"screen_n_in":null,"score":{"opus":0.2356908318322951,"gpt":0.5868320840046022,"spread":0.3511412521723071,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007130066,0.0001748076,0.0003652448,0.000776739,0.010763,0.0001346979,0.0002856495,0.0002663487,0.0001016368],"category_scores_gemma":[0.003152441,0.0001617328,0.00009920618,0.001768417,0.00005835976,0.0001827252,0.0001327658,0.0006704777,0.0003641536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007156542,"about_ca_system_score_gemma":0.003414458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008498107,"about_ca_topic_score_gemma":0.005578267,"domain_scores_codex":[0.9950827,0.001212665,0.0009027302,0.0006269903,0.0005833139,0.001591626],"domain_scores_gemma":[0.9934507,0.001931145,0.000045758,0.0004958996,0.003340689,0.0007358312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008548543,0.0001446414,0.001915167,0.004664233,0.00005857213,0.00000726585,0.1223161,0.5663378,0.000191181,0.05371614,0.2150002,0.03479393],"study_design_scores_gemma":[0.00167321,0.0008706083,0.0007332574,0.000384348,0.000004969571,8.473656e-7,0.03237401,0.1302665,0.00001495953,0.0001011503,0.8333347,0.0002414583],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5199572,0.001594113,0.1303225,0.3078609,0.001866866,0.03280397,0.002511635,0.0009004621,0.002182447],"genre_scores_gemma":[0.7566915,0.0008062482,0.1796924,0.006023977,0.003573873,0.03153006,0.004758317,0.0002491347,0.01667441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6183345,"threshold_uncertainty_score":0.9905248,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2012735611","doi":"10.1016/j.orhc.2013.10.004","title":"Intraoperative risk management of hyperparathyroidism: Modeling and testing the parathyroid hormone’s evolution as a mean reverting stochastic processes","year":2013,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Parathyroid Disorders and Treatments","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Mount Sinai Hospital; Vancouver General Hospital; York University; University of British Columbia","funders":"","keywords":"Parathyroid hormone; Medicine; Confusion; Primary hyperparathyroidism; Hyperparathyroidism; Surgery; Internal medicine; Psychology; Calcium","retraction":null,"screen_n_in":null,"score":{"opus":0.08112575593524878,"gpt":0.4131110205959838,"spread":0.331985264660735,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006380755,0.0001670612,0.0002743643,0.0001589841,0.001618388,0.00009991724,0.0001068724,0.0000544346,0.000002304784],"category_scores_gemma":[0.0007364218,0.0001144353,0.0000399315,0.0006391928,0.0001275853,0.0002316261,0.00007728115,0.0002856429,0.00001967731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002748624,"about_ca_system_score_gemma":0.0008500825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003597122,"about_ca_topic_score_gemma":0.0004607455,"domain_scores_codex":[0.9980509,0.0001855659,0.0004326352,0.0003836731,0.0004527627,0.0004944546],"domain_scores_gemma":[0.997411,0.000219537,0.00005694692,0.0003008046,0.001811619,0.0002001269],"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":[0.002986591,0.004169079,0.005069933,0.006533555,0.002923239,0.00004155207,0.2511693,0.556606,0.0257604,0.03131603,0.034263,0.07916126],"study_design_scores_gemma":[0.008652784,0.007005211,0.01126685,0.0006180907,0.0002822868,0.00007630503,0.1884356,0.7807533,0.0003421452,0.001932309,0.000135357,0.0004997833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9210585,0.05402124,0.01694741,0.001619294,0.00001596831,0.005491049,0.00006629442,0.00003073957,0.0007494927],"genre_scores_gemma":[0.9889911,0.001302603,0.007437351,0.0000845344,0.00004653357,0.001924972,0.000058581,0.00002521309,0.0001291298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2241473,"threshold_uncertainty_score":0.9996814,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4387331761","doi":"10.1016/j.orhc.2023.100409","title":"Health outcome predictive modelling in intensive care units","year":2023,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"The King's University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Western University","keywords":"Medicine; Intensive care; Logistic regression; Workload; Multinomial logistic regression; Emergency medicine; APACHE II; Receiver operating characteristic; Intensive care medicine; Intensive care unit; Machine learning; Internal medicine; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.5293279258578945,"gpt":0.5813576479679834,"spread":0.05202972211008894,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007527856,0.0001331429,0.0004132125,0.0007056537,0.0007465259,0.00004538815,0.00008638522,0.00007535307,0.00001461104],"category_scores_gemma":[0.0008156165,0.0001145315,0.00006548301,0.001493149,0.00005511295,0.00008339146,0.00006074557,0.00040186,0.00005923313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002271029,"about_ca_system_score_gemma":0.00296692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005627376,"about_ca_topic_score_gemma":0.009481604,"domain_scores_codex":[0.9975784,0.0002298807,0.0004816127,0.0004110902,0.0005262012,0.0007727669],"domain_scores_gemma":[0.9947105,0.0002739418,0.00002587614,0.0003430074,0.004258054,0.0003886162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0007509589,0.0003556907,0.1401282,0.005083817,0.0002266181,0.0001233252,0.4184805,0.2993455,0.000008394694,0.00458778,0.0890991,0.0418101],"study_design_scores_gemma":[0.007572403,0.008510729,0.05760792,0.001868649,0.00002725032,0.00001445426,0.7095011,0.1823406,0.0001169391,0.00006272004,0.03204682,0.000330422],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.734416,0.01917196,0.001742325,0.2256888,0.000475175,0.0152501,0.002278331,0.0002907323,0.000686595],"genre_scores_gemma":[0.989795,0.001524015,0.0007577835,0.002943795,0.0001100767,0.001971062,0.00250048,0.00003914362,0.00035866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2910206,"threshold_uncertainty_score":0.8506947,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1942284201","doi":"10.1016/j.orhc.2015.09.011","title":"A mathematical optimization model for efficient management of Nurses’ Quarters in a teaching and referral hospital in Hong Kong","year":2015,"lang":"en","type":"article","venue":"Operations Research for Health Care","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Workflow; Computer science; Intranet; Reservation; Quarter (Canadian coin); Process (computing); Integer programming; Operations research; Workstation; Web application; Operations management; The Internet; World Wide Web; Database; Engineering; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.1968188417889316,"gpt":0.5514352432710549,"spread":0.3546164014821233,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005236191,0.0001238166,0.000307571,0.000491089,0.001045501,0.00003809406,0.0001177032,0.0001501007,0.000004876773],"category_scores_gemma":[0.0007636255,0.0001188922,0.00003239105,0.0003798038,0.00005623294,0.0001780293,0.00005842154,0.0005812344,0.000001853546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009855103,"about_ca_system_score_gemma":0.001442536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006352015,"about_ca_topic_score_gemma":0.002082373,"domain_scores_codex":[0.9968405,0.0008510263,0.000907867,0.0003619601,0.0003790208,0.0006595823],"domain_scores_gemma":[0.9981853,0.0003212881,0.00006371611,0.0002526285,0.0009267261,0.0002503233],"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":[0.00008599486,0.0001544318,0.0003571686,0.001240115,0.000004804091,4.668276e-7,0.06693044,0.9057291,0.000001177469,0.02353415,0.0003426498,0.001619568],"study_design_scores_gemma":[0.001932112,0.0003674598,0.000169197,0.0006737822,0.000003103288,2.481185e-7,0.0757743,0.9207738,4.190228e-7,0.0001384114,0.00007466503,0.00009247589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2592257,0.0002815132,0.7169207,0.01225065,0.0001856492,0.01038483,0.0001490825,0.00002921075,0.0005726457],"genre_scores_gemma":[0.6835323,0.00004704578,0.3132365,0.0001139042,0.00003549581,0.002458472,0.0002473576,0.00002741651,0.0003015158],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4243066,"threshold_uncertainty_score":0.8041254,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}