{"id":"W1771199813","doi":"10.1186/s12955-015-0321-6","title":"The PROMIS of QALYs","year":2015,"lang":"en","type":"article","venue":"Health and Quality of Life Outcomes","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Utilities Kingston (Canada)","funders":"National Institute on Minority Health and Health Disparities; National Center for Advancing Translational Sciences; National Institutes of Health; National Cancer Institute; U.S. Public Health Service; National Institute on Aging","keywords":"Quality of life (healthcare); Quality-adjusted life year; EQ-5D; Preference; Health Utilities Index; Patient-Reported Outcomes Measurement Information System; Item response theory; Medicine; Health related quality of life; Measure (data warehouse); Psychometrics; Computer science; Risk analysis (engineering); Clinical psychology; Data mining; Cost effectiveness; Statistics; Computerized adaptive testing; Mathematics; Nursing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01417085,0.001877995,0.002161889,0.005583061,0.0006811724,0.002690931,0.002245076,0.001318513,0.04760098],"category_scores_gemma":[0.04907913,0.0005841831,0.003563891,0.00894885,0.000546983,0.002020053,0.003260104,0.002966725,0.02067727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003158623,"about_ca_system_score_gemma":0.004751958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003511193,"about_ca_topic_score_gemma":0.003347894,"domain_scores_codex":[0.9793732,0.01189847,0.002724173,0.0008422876,0.004603723,0.0005581999],"domain_scores_gemma":[0.9838955,0.006429705,0.003911988,0.001274371,0.004007367,0.0004811054],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003790196,0.0003673301,0.01652133,0.01098399,0.002392367,0.0001141564,0.0003539573,0.007477367,0.00037273,0.04177215,0.5034809,0.4123737],"study_design_scores_gemma":[0.001778187,0.00095282,0.03024091,0.0044285,0.001257071,0.0007956031,0.0002160708,0.005554955,0.0007885381,0.05124863,0.9025573,0.0001814397],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02072422,0.05977625,0.103321,0.01261473,0.002298613,0.01779844,0.55519,0.006791433,0.2214855],"genre_scores_gemma":[0.1969248,0.04782744,0.2130108,0.01136093,0.001413253,0.0856518,0.3981392,0.002217301,0.04345451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9858292,"threshold_uncertainty_score":0.1592411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6818965183014789,"score_gpt":0.5173508434163889,"score_spread":0.16454567488509,"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."}}