{"id":"W4309376528","doi":"10.2196/38095","title":"Medical Text Simplification Using Reinforcement Learning (TESLEA): Deep Learning–Based Text Simplification Approach","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Text Readability and Simplification","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"NOSM University; Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Lakehead University","keywords":"Computer science; Reinforcement learning; Readability; Annotation; Jargon; Fluency; Artificial intelligence; Natural language processing; Text simplification; Quality (philosophy); Relevance (law); Information retrieval; Linguistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001145001,0.001158785,0.0008828839,0.001281376,0.0003573777,0.0007337644,0.00135819,0.0009598118,0.003932158],"category_scores_gemma":[0.004704001,0.0003562903,0.0011072,0.0006531694,0.0005552687,0.001173126,0.001060142,0.001836127,0.001831685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009002407,"about_ca_system_score_gemma":0.001221297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003255776,"about_ca_topic_score_gemma":0.004961708,"domain_scores_codex":[0.9991886,0.0002334801,0.00007741051,0.0002142565,0.0002221656,0.00006409428],"domain_scores_gemma":[0.9977736,0.001118845,0.0002649067,0.0002898707,0.0004401463,0.0001127015],"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":[0.0003941278,0.0002666233,0.00173729,0.000519259,0.0001248999,0.0005083411,0.0003000023,0.2192606,0.03152556,0.003888818,0.01890508,0.7225694],"study_design_scores_gemma":[0.0000465706,0.0001306864,0.0004799437,0.00003702727,0.0000483476,0.0001676637,0.00004138877,0.9755407,0.0131557,0.004213457,0.00611904,0.00001943654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0357629,0.001348457,0.9495124,0.0007016591,0.0002129722,0.0002494617,0.000456736,0.009059869,0.002695464],"genre_scores_gemma":[0.3968108,0.001057976,0.5815251,0.0009525192,0.0003400235,0.0004027301,0.003491767,0.0008171548,0.01460189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003932158,"threshold_uncertainty_score":0.01315433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02522489423537592,"score_gpt":0.2855421848158192,"score_spread":0.2603172905804432,"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."}}