{"id":"W3005662983","doi":"10.2196/16806","title":"Noninvasive Hemoglobin Level Prediction in a Mobile Phone Environment: State of the Art Review and Recommendations","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Photoplethysmogram; Biosignal; Data collection; Mobile phone; Machine learning; Artificial intelligence; Real-time computing; Data mining; Wireless; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0001855787,0.0001192932,0.0002418736,0.00003716898,0.00006830556,0.000006562527,0.00006336285,0.00003387257,0.00002327931],"category_scores_gemma":[0.00001895853,0.0001035009,0.00002081207,0.0002115627,0.00003839593,0.00009895454,0.00004519472,0.0002280136,0.00001132724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001023533,"about_ca_system_score_gemma":0.00005130546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002111987,"about_ca_topic_score_gemma":0.00001907899,"domain_scores_codex":[0.9990724,0.00005144018,0.000364306,0.0001769749,0.0001054386,0.0002294606],"domain_scores_gemma":[0.9995228,0.00005406204,0.00008548681,0.0001264676,0.00001079854,0.0002003647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002301033,0.0006803708,0.1877374,0.151965,0.0002119392,0.00003129483,0.02947531,0.006389229,0.03936268,0.000241023,0.1030757,0.4805999],"study_design_scores_gemma":[0.0134257,0.003545073,0.7104067,0.01511532,0.0007242544,0.00024277,0.003139317,0.007578719,0.03896935,0.001791587,0.20225,0.002811163],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.938776,0.04703773,0.001088584,0.008383797,0.0003540106,0.003158803,0.0006164743,0.0001217166,0.0004628886],"genre_scores_gemma":[0.927323,0.07102413,0.000441267,0.0008840145,0.00006174759,0.0001888568,0.00001870499,0.00002336086,0.00003493204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5226693,"threshold_uncertainty_score":0.4220646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03994369279344639,"score_gpt":0.2896646163011731,"score_spread":0.2497209235077267,"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."}}