{"id":"W4310370637","doi":"10.2196/39849","title":"Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Long-Term Effects of COVID-19","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; Coronavirus disease 2019 (COVID-19); Anxiety; Medicine; Psychology; Psychiatry; Computer science; World Wide Web; Disease; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0008874345,0.0002906542,0.0004555651,0.004717451,0.000679758,0.00124169,0.0003544051,0.0007126769,0.001426672],"category_scores_gemma":[0.003400382,0.0001529933,0.0006365626,0.002848168,0.0003025373,0.0007892727,0.0007597861,0.0004390512,0.0005243202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001156343,"about_ca_system_score_gemma":0.0008930571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.021933,"about_ca_topic_score_gemma":0.02131647,"domain_scores_codex":[0.9993961,0.0001561275,0.00008829029,0.0001312316,0.00009574895,0.0001324937],"domain_scores_gemma":[0.9973424,0.000910357,0.000912762,0.0001032827,0.0003918533,0.0003393283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001460393,0.0001157241,0.9884135,0.0000929028,0.00004573296,0.0004760664,0.002139554,0.00007917274,0.000267221,0.00005186396,0.0009868577,0.007185527],"study_design_scores_gemma":[0.000009171924,0.0001407248,0.9906994,0.00005001203,0.00003192006,0.0004955183,0.006027338,0.0008124894,0.0001484025,0.00003800783,0.001529531,0.0000174329],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966647,0.0002087271,0.0001606247,0.0001545679,0.0000062063,0.00005193038,0.002279745,0.00001421579,0.0004592765],"genre_scores_gemma":[0.9949412,0.000291033,0.0004552273,0.000107241,0.0000335949,0.0001285589,0.003535125,0.000006613161,0.0005013497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.021933,"threshold_uncertainty_score":0.04361063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04400189608833391,"score_gpt":0.3651983879838027,"score_spread":0.3211964918954688,"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."}}