{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":2,"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","author_layer_release":"2026-06-26"},"query_hash":"36d1346b22cc","filters":{"venue":"AJE Advances Research in Epidemiology"}},"results":[{"id":"W4413956268","doi":"10.1093/ajeadv/uuaf008","title":"Fentanyl concentrations in unregulated opioids and the blood of drug toxicity decedents","year":2025,"lang":"en","type":"article","venue":"AJE Advances Research in Epidemiology","topic":"Opioid Use Disorder Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Vancouver Coastal Health; University of British Columbia Hospital; British Columbia Centre on Substance Use; Provincial Health Services Authority; Culex Environmental; University of British Columbia","funders":"","keywords":"Fentanyl; Medicine; Toxicity; Drug; Anesthesia; Emergency medicine; Pharmacology; Internal medicine","authors":[{"name":"Samuel Tobias","is_ca":true},{"name":"Andrew W. Tu","is_ca":true},{"name":"Aaron M. Shapiro","is_ca":true},{"name":"Sandrine A. M. Mérette","is_ca":true},{"name":"Mark Lysyshyn","is_ca":true},{"name":"Evan Wood","is_ca":true},{"name":"Jane A. Buxton","is_ca":true},{"name":"Lianping Ti","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05362236926943703,"gpt":0.4530249178019112,"spread":0.3994025485324742,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008108984,0.0001776584,0.0002674935,0.0006464232,0.0005739527,0.001096441,0.0004613158,0.0002535027,0.001767491],"category_scores_gemma":[0.005221844,0.0001455063,0.0001777275,0.001285771,0.000353075,0.0002835823,0.000375408,0.0006366619,0.0002131731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001900372,"about_ca_system_score_gemma":0.001263112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2532136,"about_ca_topic_score_gemma":0.3155806,"domain_scores_codex":[0.9989176,0.0002141583,0.00008364315,0.0002017645,0.0004126441,0.0001702543],"domain_scores_gemma":[0.9960646,0.000598967,0.001535589,0.0001953556,0.001377702,0.0002278462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001707552,0.0000296456,0.9912061,0.00003823247,0.00004813002,0.0001664342,0.0004041546,0.0001442468,0.0008323417,0.00008362487,0.0003791536,0.006497119],"study_design_scores_gemma":[0.000002271964,0.00005485599,0.996445,0.00003728246,0.00002442065,0.0002892375,0.0007556153,0.0006329375,0.0006778377,0.00006796537,0.001006552,0.000005996747],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962374,0.0007308412,0.0005060661,0.0002868699,0.00001647703,0.00001386163,0.001195765,0.000008975357,0.001003674],"genre_scores_gemma":[0.9976367,0.0003914455,0.0004123452,0.0001172526,0.00001320464,0.000008343577,0.0006945877,0.000005008002,0.0007211024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2532136,"threshold_uncertainty_score":0.5034795,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414396056","doi":"10.1093/ajeadv/uuaf010","title":"ICD-10 code embedding for patient matching: a first step toward applications in causal inference from hospital discharge data","year":2025,"lang":"en","type":"article","venue":"AJE Advances Research in Epidemiology","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"University of Bern","keywords":"Propensity score matching; Causal inference; Matching (statistics); Set (abstract data type); Inference; Observational study; Population; Pooling; Code (set theory)","authors":[{"name":"Judith A. Bouman","is_ca":false},{"name":"André Moser","is_ca":false},{"name":"Martin Wohlfender","is_ca":false},{"name":"Alexandra Leichtle","is_ca":true},{"name":"Guido Beldi","is_ca":false},{"name":"Olga Endrich","is_ca":false},{"name":"Julien Riou","is_ca":false},{"name":"Christian L. Althaus","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4973699652473476,"gpt":0.628304726194639,"spread":0.1309347609472914,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01077907,0.0006864514,0.0007387098,0.002118389,0.000449284,0.001521854,0.001173049,0.001011069,0.007540751],"category_scores_gemma":[0.06083006,0.0004478061,0.0009617895,0.001851642,0.0005539429,0.001671206,0.003058439,0.002050578,0.001913639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004928565,"about_ca_system_score_gemma":0.001889817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002118496,"about_ca_topic_score_gemma":0.003035651,"domain_scores_codex":[0.9928902,0.004976811,0.0004398926,0.0007399825,0.0008217813,0.0001312556],"domain_scores_gemma":[0.9807967,0.01349688,0.0008623033,0.00323406,0.001274161,0.0003358914],"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":[0.0006582465,0.0004632606,0.03898327,0.0005851646,0.0008566644,0.000214208,0.0004884484,0.04776252,0.004292028,0.0415308,0.0342577,0.8299077],"study_design_scores_gemma":[0.000299652,0.000340338,0.008209698,0.0002532006,0.0001180163,0.0003782978,0.0002009019,0.8501323,0.004677325,0.1122121,0.02309474,0.00008343508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01391714,0.0004101807,0.9786472,0.001243603,0.0001915193,0.0002868197,0.001805031,0.0027741,0.0007242984],"genre_scores_gemma":[0.1117454,0.0002680761,0.882879,0.000544061,0.0002035658,0.0004799596,0.002793267,0.0004118348,0.0006748354],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01077907,"threshold_uncertainty_score":0.05700582,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}