{"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":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.005545482,0.0001297795,0.0002754345,0.000784753,0.0003803826,0.0004012807,0.001441533,0.000164179,0.00002707925],"category_scores_gemma":[0.0007832507,0.00009844806,0.00006809441,0.001032118,0.0001857258,0.0004325179,0.0009512628,0.00281348,0.000008753555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002081232,"about_ca_system_score_gemma":0.0009123532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001459836,"about_ca_topic_score_gemma":0.0002076069,"domain_scores_codex":[0.9954417,0.0006042019,0.0005275056,0.0003048612,0.002615109,0.0005065724],"domain_scores_gemma":[0.997611,0.001007958,0.0001773451,0.0002857284,0.0005296409,0.0003883369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009505072,0.0001033683,0.533747,0.0002194657,0.00006891108,0.002030768,0.0006186547,0.00002523802,0.00007917824,0.003045488,0.0002435734,0.4597233],"study_design_scores_gemma":[0.0109772,0.009935903,0.434602,0.02319205,0.00007480734,0.03007623,0.005021237,0.4288568,0.002920981,0.01810212,0.03481309,0.001427551],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9098786,0.003230402,0.07740745,0.005368465,0.001137201,0.0002283654,8.33867e-7,0.00004605742,0.002702676],"genre_scores_gemma":[0.9648381,0.0005465928,0.033291,0.0003297504,0.0005054746,0.000003715026,9.038437e-7,0.00001466524,0.0004697887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4582957,"threshold_uncertainty_score":0.999487,"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."}}