{"id":"W4213182417","doi":"10.2196/35181","title":"Impact of a Conformité Européenne (CE) Certification–Marked Medical Software Sensor on COVID-19 Pandemic Progression Prediction: Register-Based Study Using Machine Learning Methods","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Business Finland; ITEA","keywords":"Pandemic; Triage; Medicine; Population; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Machine learning; Linear regression; Computer science; Medical emergency; Internal medicine","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":[0.006528324,0.0006720331,0.0005494433,0.001295792,0.0003535877,0.001347119,0.00104413,0.001034614,0.001137925],"category_scores_gemma":[0.01977054,0.0002944764,0.0011197,0.0008794424,0.0004931195,0.001082668,0.00107132,0.0008553687,0.0005344808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167475,"about_ca_system_score_gemma":0.001001667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02335561,"about_ca_topic_score_gemma":0.01339606,"domain_scores_codex":[0.9964387,0.001502702,0.000296793,0.0009704364,0.0005206232,0.0002706148],"domain_scores_gemma":[0.9852635,0.00933606,0.001992639,0.001192641,0.001714104,0.000501143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008730633,0.0009183119,0.9409741,0.00006921414,0.0002190928,0.0001773984,0.0002053415,0.02753142,0.0007650928,0.0002322248,0.001129282,0.02690545],"study_design_scores_gemma":[0.00007696549,0.001889416,0.4470875,0.00006270638,0.0001746561,0.0002537495,0.0006066826,0.5460942,0.001939866,0.0003084164,0.001450592,0.00005532768],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954152,0.00008798885,0.002672232,0.0001569276,0.00003599218,0.00005291542,0.0009362164,0.0000811166,0.0005615011],"genre_scores_gemma":[0.9962165,0.00003631184,0.00186174,0.0000520479,0.00001850979,0.00003354267,0.001535828,0.00001071858,0.0002348247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02335561,"threshold_uncertainty_score":0.04643929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2466402008104026,"score_gpt":0.5525301449961731,"score_spread":0.3058899441857705,"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."}}