{"id":"W4306730885","doi":"10.2196/37945","title":"The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Schweizerische Multiple Sklerose Gesellschaft; Universität Zürich; Multiple Sclerosis Society","keywords":"Computer science; Data science; Python (programming language); Natural language processing; Pipeline (software); Artificial intelligence; Scale (ratio); Information retrieval","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001185976,0.00005839151,0.0001187482,0.0000549755,0.001660781,0.00003336847,0.0004552138,0.00002072273,0.00008984258],"category_scores_gemma":[0.0002986649,0.00003113216,0.00005313058,0.0008144964,0.0006535306,0.000112706,0.000112126,0.0001759173,5.288924e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008708135,"about_ca_system_score_gemma":0.0003383208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001167948,"about_ca_topic_score_gemma":0.004215565,"domain_scores_codex":[0.998193,0.0001196859,0.0003340726,0.00005645967,0.001156985,0.000139763],"domain_scores_gemma":[0.9986115,0.0007872679,0.0003323917,0.0001172216,0.00006451175,0.00008713041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004238033,0.0000495039,0.006821778,0.0001169573,0.0000267822,5.054919e-7,0.9522426,0.0007296851,0.000008655096,0.001191271,0.0002174742,0.03855239],"study_design_scores_gemma":[0.0002919031,0.00002423885,0.005762472,0.00002582814,0.00001637537,0.000001558537,0.8850864,0.09477361,0.000044032,0.00005358933,0.01383729,0.00008268644],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918146,0.0002125063,0.003077408,0.002744218,0.00005417385,0.0003570475,0.000005180435,0.00003645169,0.001698429],"genre_scores_gemma":[0.9986221,0.00001525994,0.0006927127,0.0002387824,0.0000456756,0.0002054833,0.000004902111,0.000002735289,0.0001723073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09404392,"threshold_uncertainty_score":0.9996389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02600592410652876,"score_gpt":0.353403194233656,"score_spread":0.3273972701271272,"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."}}