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Validation of the PROMIS-57 and PROMIS-29 Profile Questionnaires in Kidney Transplant Recipients

2018· article· en· W2884087451 on OpenAlexaffabout
Evan Tang, Aarushi Bansal, Sarah Cao, Nathaniel Edwards, Madeline Li, Márta Novák, István Mucsi

Bibliographic record

VenueTransplantation · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreToronto General Hospital
Fundersnot available
KeywordsCronbach's alphaConfirmatory factor analysisAnxietyQuality of life (healthcare)MedicineClinical psychologyConstruct validityIntraclass correlationPhysical therapyPatient-Reported Outcomes Measurement Information SystemConvergent validityPatient Health QuestionnairePsychometricsPsychiatryStructural equation modelingComputerized adaptive testingInternal consistencyStatistics

Abstract

fetched live from OpenAlex

Background The Patient Reported Outcomes Measurement Information System (PROMIS) aims to address the lack of generalizable and universal measure of patient reported outcomes to assess many health-related quality of life domains. It has undergone extensive psychometric testing for validity and reliability but it has not been tested among patients with chronic kidney disease. Here, we validate the PROMIS-57 and the PROMIS-29, questionnaire among kidney transplant recipients. Methods A cross-sectional, convenience sample of stable kidney transplant recipients was recruited. Each participant completed the PROMIS-57, a 57-question instrument that measures 7 domains – physical function, anxiety, depression, fatigue, pain, sleep disturbance, and social functioning – alongside validated legacy questionnaires (Patient Health Questionnaire (PHQ9), General Anxiety Disorder (GAD7), Edmonton Symptom Assessment Scale (ESAS), and Kidney Disease Quality of Life (KDQoL-36)). PROMIS-29, a 29-question instrument is nested within PROMIS-57 and measures the same domains. Structural validity of PROMIS was assessed using confirmatory factor analysis and reported using the Tucker Lewis Index (TLI) and Comparative fit index (CFI). Construct validity was assessed with known group comparisons. Internal consistency was assessed with Cronbach’s α and convergent validity was assessed through Spearman’s Rho. Test-retest reliability was assessed through the intraclass coefficient (ICC). Results Mean (±SD) age of the 179 participants was 51 (±17), 57% were male, and 55% Caucasian. Internal consistency of each domain was high (Cronbach’s α>0.90 for each). Confirmatory factor analysis showed good structural validity (TLI and CFI > 0.95 for most domain measures). PROMIS anxiety demonstrated strong correlation with GAD7 (PROMIS-57: rho=0.760, 95%CI:0.683-0.820; PROMIS-29: rho=0.730, 95%CI:0.645-0.798). PROMIS depression demonstrated correlation with PHQ9 (PROMIS-57: rho=0.676, 95%CI:0.578-0.755; PROMIS-29: rho=0.634, 95%CI:0.526-0.721). PROMIS physical function demonstrated strong correlation with the KDQol-36 physical composite score (PROMIS-57: rho=0.806, 95%CI:0.745-0.853; PROMIS-29: rho=0.789, 95%CI:0.723-0.840). PROMIS fatigue demonstrated strong correlation with ESAS fatigue (PROMIS-57: rho=0.722, 95%CI:0.636-0.790; PROMIS-29: rho=0.725, 95%CI:0.640-0.792). Test-retest reliability indicated good agreement, with ICC>0.6 for all. Known group comparisons also supported validity. Conclusions Our results confirmed that the PROMIS-57 and the PROMIS-29 profile are highly reliable and valid instrument among kidney transplant recipients. We propose it is a valuable tool to assess domains of the disease experience that are relevant and important for patients. Further studies are needed to examine the validity and reliability of computer adaptive testing to reduce questionnaire burden.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.284
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations6
Published2018
Admission routes2
Has abstractyes

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