{"id":"W4366333686","doi":"10.1002/lrh2.10368","title":"Striking a match between <scp>FHIR</scp>‐based patient data and <scp>FHIR</scp>‐based eligibility criteria","year":2023,"lang":"en","type":"article","venue":"Learning Health Systems","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Centre for Advancing Health Outcomes","funders":"","keywords":"JSON; Computer science; Matching (statistics); JavaScript; Software; Function (biology); Resource (disambiguation); World Wide Web; Medicine; Operating system; Computer network","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":[0.01190209,0.001454496,0.001003831,0.003716357,0.0009633069,0.003776465,0.001430823,0.002104489,0.1358937],"category_scores_gemma":[0.05165785,0.0009775381,0.001074038,0.002111692,0.0006991989,0.003283887,0.005089773,0.001138774,0.05141924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001911697,"about_ca_system_score_gemma":0.003256304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00452229,"about_ca_topic_score_gemma":0.005389905,"domain_scores_codex":[0.9933756,0.001716487,0.001019427,0.001303246,0.00221067,0.0003745274],"domain_scores_gemma":[0.9734311,0.01580958,0.002380695,0.004111939,0.00303327,0.00123344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003156824,0.0004803763,0.02810488,0.001579645,0.0001541802,0.0008606412,0.0009013941,0.00202774,0.006188453,0.009891785,0.7659162,0.180738],"study_design_scores_gemma":[0.001608891,0.0008094736,0.07732811,0.00155626,0.0001679483,0.001946954,0.001048863,0.08585628,0.07881632,0.04141333,0.7088699,0.0005777028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.03028682,0.0003933035,0.2399205,0.006200759,0.0007872814,0.004824035,0.2038339,0.4370444,0.07670898],"genre_scores_gemma":[0.291608,0.0005372545,0.3504443,0.007581521,0.0008483832,0.005265524,0.2166367,0.07595565,0.05112269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1358937,"threshold_uncertainty_score":0.4546099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08013222294958353,"score_gpt":0.3912649578392925,"score_spread":0.3111327348897089,"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."}}