{"id":"W7165679016","doi":"10.2196/86583","title":"A Network Visualization Query System for Multi-Drug Compatibility Based on a WeChat Mini Program: A Preliminary Usability and Efficiency Evaluation (Preprint)","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Usability; Compatibility (geochemistry); Visualization; Pairwise comparison; Scalability; System usability scale; Mobile device","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":[0.007506843,0.001164695,0.0008212344,0.001046186,0.0004649251,0.001124276,0.001526396,0.0006768106,0.006550321],"category_scores_gemma":[0.01945214,0.0004113412,0.0006462812,0.000697683,0.0003886883,0.002092131,0.001533252,0.0007182221,0.000845784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007228226,"about_ca_system_score_gemma":0.001280917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004416523,"about_ca_topic_score_gemma":0.00277311,"domain_scores_codex":[0.9968908,0.001684446,0.000332263,0.000399072,0.0005480128,0.0001454116],"domain_scores_gemma":[0.9753103,0.01966267,0.0005157251,0.001151867,0.002750658,0.0006088101],"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":[0.01658181,0.01045903,0.04242137,0.004152121,0.000761102,0.001951253,0.01300198,0.01391572,0.1465449,0.001549734,0.04028673,0.7083743],"study_design_scores_gemma":[0.006590058,0.04478757,0.2757228,0.0008352071,0.001521345,0.003729896,0.00680634,0.4160688,0.1716529,0.002174628,0.06909586,0.001014493],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8609509,0.0003975756,0.10455,0.0005918735,0.00007100117,0.006742301,0.003202489,0.02108638,0.002407435],"genre_scores_gemma":[0.7206112,0.000390645,0.2622289,0.0003655135,0.00005774215,0.0060502,0.006224655,0.001325243,0.002745786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007506843,"threshold_uncertainty_score":0.03970045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1098799168526952,"score_gpt":0.4779369691758859,"score_spread":0.3680570523231906,"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."}}