{"id":"W2107636880","doi":"10.1002/qre.1084","title":"Reliability analysis of maintenance data for complex medical devices","year":2010,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Quality and Safety in Healthcare","field":"Health Professions","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Censoring (clinical trials); Reliability engineering; Reliability (semiconductor); Computer science; Preventive maintenance; Data mining; Engineering; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009560827,0.0001675517,0.0005944013,0.000157279,0.000230477,0.000009646228,0.0008625795,0.0004138719,0.0008793584],"category_scores_gemma":[0.01670057,0.0001449191,0.0001534926,0.000313883,0.0001994262,0.0001994547,0.0003969868,0.0009183096,0.000004604247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006996902,"about_ca_system_score_gemma":0.0002445934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001332003,"about_ca_topic_score_gemma":0.001170243,"domain_scores_codex":[0.9966496,0.0003216668,0.001465593,0.0005901408,0.0006245105,0.0003485378],"domain_scores_gemma":[0.9919261,0.005525667,0.0003360703,0.001205346,0.0007678993,0.0002388594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00066903,0.000672875,0.7226011,0.006985028,0.001325666,0.000001884671,0.003028437,0.004295896,0.001274759,0.2515462,0.00333932,0.004259848],"study_design_scores_gemma":[0.0004410572,0.0000209513,0.6886152,0.00007457931,0.00009906344,6.250871e-7,0.000282759,0.2491148,0.000004686653,0.001089089,0.06010808,0.0001490637],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9101269,0.00003376826,0.05619758,0.02520227,0.002737728,0.0008359973,0.004072445,0.0001490948,0.0006442317],"genre_scores_gemma":[0.9823561,0.00004907287,0.01497818,0.0008385758,0.0003239864,0.00006794708,0.001272057,0.00001310193,0.0001009802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2504571,"threshold_uncertainty_score":0.9915822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.150404268543627,"score_gpt":0.4940515820583954,"score_spread":0.3436473135147684,"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."}}