{"id":"W4416401342","doi":"10.1177/00220345251383863","title":"LinkMD: Linking Medical and Dental Records with 4 Linking Algorithms","year":2025,"lang":"en","type":"article","venue":"Journal of Dental Research","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Temple University","keywords":"Medical record; Probabilistic logic; Interoperability; Similarity (geometry); Record linkage; Health records; Missing data; Linkage (software); Masking (illustration)","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":[0.01817552,0.001696166,0.001435663,0.007503511,0.001886504,0.004183295,0.0036576,0.003005243,0.006355622],"category_scores_gemma":[0.05160268,0.001788382,0.002243534,0.004966459,0.001000085,0.004135847,0.006423149,0.002292873,0.002181984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002090569,"about_ca_system_score_gemma":0.003677813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00694448,"about_ca_topic_score_gemma":0.005703895,"domain_scores_codex":[0.9880154,0.004680418,0.001675356,0.003002433,0.002283001,0.0003434704],"domain_scores_gemma":[0.9839489,0.01084232,0.001403835,0.001853674,0.001741161,0.000210213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001530403,0.0009997963,0.0463125,0.001087706,0.001608098,0.0004139398,0.00110556,0.09407108,0.004312426,0.01465995,0.03879515,0.7951033],"study_design_scores_gemma":[0.0006667839,0.0003341274,0.00679412,0.0002252742,0.0003765189,0.0004930422,0.000355807,0.908525,0.01727242,0.02844848,0.03633296,0.0001754887],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04067588,0.0006538606,0.9012945,0.0006899937,0.0001870527,0.001660806,0.004212804,0.04788652,0.002738594],"genre_scores_gemma":[0.06419185,0.0001478135,0.9258145,0.000301718,0.00004933331,0.001603699,0.005943701,0.0009799791,0.0009673776],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01817552,"threshold_uncertainty_score":0.09612244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04494204538395766,"score_gpt":0.427835516684398,"score_spread":0.3828934713004403,"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."}}