{"id":"W4409350830","doi":"10.2196/60900","title":"Leveraging AI to Drive Timely Improvements in Patient Experience Feedback: Algorithm Validation","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute for Health and Care Research","keywords":"Preprint; Computer science; Algorithm; Artificial intelligence; Machine learning; World Wide Web","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":[0.01589046,0.00165757,0.000939708,0.001560595,0.0007997375,0.002035745,0.002219854,0.001378714,0.002056413],"category_scores_gemma":[0.05579137,0.0004743467,0.0008689802,0.001072524,0.0008202083,0.002114806,0.001911964,0.002662551,0.001437027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001593996,"about_ca_system_score_gemma":0.003676189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007498548,"about_ca_topic_score_gemma":0.006758737,"domain_scores_codex":[0.9940203,0.00308132,0.0006406536,0.001258173,0.0007197218,0.0002797039],"domain_scores_gemma":[0.9510233,0.03609064,0.001863647,0.003073097,0.007530911,0.0004182926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00139883,0.001377831,0.06491344,0.0006459331,0.0004404976,0.000277242,0.0014393,0.2552146,0.009260554,0.002049831,0.008571027,0.6544108],"study_design_scores_gemma":[0.0000904669,0.0002711923,0.003022671,0.00006597047,0.00005354755,0.00006796711,0.0002174416,0.9860359,0.006459523,0.001908964,0.001781913,0.00002445964],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3054517,0.0009775435,0.6693493,0.002073559,0.0003240157,0.00266967,0.001409967,0.01314824,0.004596085],"genre_scores_gemma":[0.5473668,0.0001752096,0.4471931,0.0004227452,0.00006142822,0.001458018,0.001815449,0.0002264036,0.001280935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01589046,"threshold_uncertainty_score":0.08403784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04849665135995058,"score_gpt":0.4152713576240971,"score_spread":0.3667747062641465,"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."}}