{"id":"W4389476129","doi":"10.2196/preprints.55010","title":"Figure Correction: Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach (Preprint)","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Terminology; Normalization (sociology); Computer science; Natural language processing; Preprint; Artificial intelligence; Named-entity recognition; Sentiment analysis; F1 score; Language model; Information retrieval; Machine learning; Data science; Task (project management); World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002669317,0.001801176,0.00115931,0.003543741,0.002187891,0.002966704,0.002553763,0.004677096,0.1898662],"category_scores_gemma":[0.07920338,0.0006840865,0.001554042,0.002315753,0.001829343,0.002295244,0.002016397,0.005511853,0.05914529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002433803,"about_ca_system_score_gemma":0.003407842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01814691,"about_ca_topic_score_gemma":0.0165071,"domain_scores_codex":[0.9969121,0.000580708,0.0006008858,0.000533987,0.001059014,0.0003133061],"domain_scores_gemma":[0.9539123,0.02042709,0.002200372,0.003624304,0.01864341,0.001192576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001522246,0.000007782293,0.0005392191,0.0003564578,0.00002636726,0.000802517,0.0001323113,0.00012368,0.0003722075,0.0008991866,0.9865558,0.0100323],"study_design_scores_gemma":[0.0001332177,0.00004925867,0.005142744,0.0007275208,0.00008227181,0.003237252,0.0005540274,0.002169171,0.00248103,0.00372551,0.9816065,0.00009143401],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.003767,0.0009969653,0.009415106,0.1239773,0.8087054,0.000274142,0.03138341,0.009288703,0.01219189],"genre_scores_gemma":[0.1454691,0.005379143,0.05976079,0.1006566,0.1993537,0.001544638,0.03520043,0.0188081,0.4338277],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1898662,"threshold_uncertainty_score":0.6351656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07853407279349083,"score_gpt":0.3787596431613832,"score_spread":0.3002255703678923,"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."}}