{"id":"W3185442588","doi":"10.2196/25531","title":"Evaluation of Three Feasibility Tools for Identifying Patient Data and Biospecimen Availability: Comparative Usability Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bundesministerium für Bildung und Forschung; Friedrich-Alexander-Universität Erlangen-Nürnberg","keywords":"Usability; Computer science; Health informatics; Heuristic evaluation; Informatics; Cognitive walkthrough; User interface; User Friendly; Web usability; Data science; World Wide Web; Health care; Human–computer interaction; Engineering","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02427202,0.0001837716,0.0007377235,0.00005434804,0.0003942406,0.0000286811,0.0004179262,0.0002659955,0.0006356199],"category_scores_gemma":[0.01091685,0.0001516028,0.00004785136,0.0003042792,0.0001828837,0.0007203008,0.0007574432,0.0007025902,0.00002876744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007658761,"about_ca_system_score_gemma":0.004972797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001101203,"about_ca_topic_score_gemma":0.003854704,"domain_scores_codex":[0.9915149,0.002221475,0.002921137,0.0003621182,0.002433025,0.0005472996],"domain_scores_gemma":[0.9914166,0.003716432,0.0008604688,0.001559525,0.002100683,0.0003462413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002850477,0.003383368,0.5776675,0.0112393,0.0004499325,0.000001686535,0.1883116,0.000005680177,0.00004229768,0.0005651356,0.008165208,0.2098832],"study_design_scores_gemma":[0.01182061,0.001849917,0.330472,0.001397756,0.0004960372,0.000008811026,0.3591593,0.2809768,0.0001148257,0.004886537,0.008241646,0.0005757989],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985247,0.0002399193,0.001795928,0.000311256,0.0005076017,0.01042044,0.0002297754,0.00004161557,0.001206444],"genre_scores_gemma":[0.9970051,0.00001198111,0.0014667,0.0002439612,0.0001065244,0.0009233917,0.0002188159,0.0000101707,0.00001336037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2809711,"threshold_uncertainty_score":0.9974146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5380825488161064,"score_gpt":0.5724558043353799,"score_spread":0.0343732555192735,"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."}}