{"id":"W4402405015","doi":"10.23889/ijpds.v9i5.2553","title":"Free Text Analysis: Identification of adverse drug events in clinical notes","year":2024,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Manitoba Health","funders":"","keywords":"Identification (biology); Drug; Computer science; Drug reaction; Adverse effect; Natural language processing; Medicine; Pharmacology; Biology","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":[0.004812552,0.001116314,0.0005875964,0.008603912,0.0005263903,0.001737833,0.001015047,0.001020934,0.003697031],"category_scores_gemma":[0.03700461,0.0002247011,0.0008070067,0.003582333,0.0005162693,0.002085843,0.001122381,0.0008362672,0.001314927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009464307,"about_ca_system_score_gemma":0.001410936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003362642,"about_ca_topic_score_gemma":0.004712855,"domain_scores_codex":[0.9943277,0.002029553,0.001191256,0.001163287,0.001106486,0.0001816935],"domain_scores_gemma":[0.9386796,0.04425001,0.009273827,0.002579304,0.004586529,0.0006306574],"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":[0.0025321,0.0008033736,0.2952484,0.007413631,0.0006366372,0.00405221,0.003323443,0.01109503,0.03357897,0.002124356,0.0296815,0.6095104],"study_design_scores_gemma":[0.0004432559,0.001632339,0.620885,0.002147405,0.0006995962,0.007031996,0.005057612,0.2236675,0.06139747,0.01527566,0.06131863,0.0004435988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7344908,0.004702381,0.1434663,0.003747224,0.0005222507,0.002937163,0.09539331,0.007415066,0.007325453],"genre_scores_gemma":[0.7580188,0.001333311,0.178858,0.0006593834,0.0004176277,0.001156446,0.05640386,0.0002074591,0.002945068],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008603912,"threshold_uncertainty_score":0.02545154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06472439430834917,"score_gpt":0.4461520205106619,"score_spread":0.3814276262023127,"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."}}