{"id":"W3177364780","doi":"10.1186/s40708-021-00134-4","title":"A deep neural network approach for sentiment analysis of medically related texts: an analysis of tweets related to concussions in sports","year":2021,"lang":"en","type":"article","venue":"Brain Informatics","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital; Toronto Metropolitan University","funders":"Ontario Research Foundation","keywords":"Concussion; Context (archaeology); Convolutional neural network; Psychology; Sentiment analysis; Artificial intelligence; Injury prevention; Poison control; Computer science; Machine learning; Computer security; Applied psychology; Medicine; Medical emergency; History","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005920474,0.0009868949,0.000372904,0.001696427,0.0004459528,0.0005767128,0.0004073103,0.0007045745,0.001322721],"category_scores_gemma":[0.001460584,0.0001956562,0.0006562662,0.0009170767,0.0002054938,0.0007714648,0.0005063325,0.0007532276,0.001048969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006667595,"about_ca_system_score_gemma":0.0004879857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006866714,"about_ca_topic_score_gemma":0.01114448,"domain_scores_codex":[0.9996297,0.00006608119,0.00004957788,0.00009056534,0.00009464383,0.00006936631],"domain_scores_gemma":[0.9995201,0.0001801112,0.00007640509,0.00002286395,0.0001698864,0.00003073928],"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":[0.001425779,0.00108043,0.08082016,0.000862679,0.0004044672,0.002582234,0.001517318,0.03353621,0.09892306,0.001355226,0.03859861,0.7388939],"study_design_scores_gemma":[0.00003268805,0.0002661381,0.05893905,0.00009692236,0.0001216414,0.0004292677,0.0012849,0.9019971,0.02193021,0.002329442,0.01251734,0.00005518584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.852584,0.002629397,0.1107102,0.002676965,0.001048232,0.0006944403,0.01607905,0.004662458,0.008915349],"genre_scores_gemma":[0.9036906,0.0008649665,0.07408416,0.0003467597,0.0002825688,0.0002496174,0.0135377,0.00009445599,0.006849183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006866714,"threshold_uncertainty_score":0.01365352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02914208344683134,"score_gpt":0.3393988212311144,"score_spread":0.310256737784283,"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."}}