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Accuracy of Promis-57 Depression and Anxiety Scales Compared to Legacy Instruments Among Kidney Transplant Recipients

2018· article· en· W2884726026 on OpenAlexaff
Aarushi Bansal, Evan Tang, Farzad Khalafi, Heather Ford, Madeline Li, Márta Novák, István Mucsi

Bibliographic record

VenueTransplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsPatient Health QuestionnaireAnxietyPatient-Reported Outcomes Measurement Information SystemDepression (economics)MedicineClinical psychologyKidney transplantHospital Anxiety and Depression ScaleMetric (unit)Physical therapyKidney transplantationPsychometricsPsychiatryTransplantationInternal medicineComputerized adaptive testingDepressive symptoms

Abstract

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Background Depression and anxiety are frequent among kidney transplant recipients (KTRs). Patient reported outcome measures are used to assess these constructs, but concerns remain about measurement precision and questionnaire burden. The NIH PROsetta Stone project developed a common metric system which associates scores from scales measuring similar concepts across known legacy tools with the Patient Reported Outcomes Measurement System (PROMIS-57). Here, we evaluate the accuracy of the depression and anxiety domains of the PROMIS-57 profile questionnaire among KTRs. Methods Participants of this cross-sectional, convenience sample of stable KTRs completed the PROMIS-57 (includes PROMIS-29), Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9) questionnaires. Raw scores of legacy tools (GAD-7 and PHQ-9) were converted to PROMIS T-scores using PROsetta Stone© crosswalk files. Spearman correlations were conducted between calculated PROMIS (from legacy scores), and reported PROMIS-57 scores. The cut off score of 10 on GAD-7 and PHQ-9 legacy scales were used to indicate clinically significant (moderate to severe) depression or anxiety, respectively. The corresponding PROsetta stone cut off scores on the reported PROMIS-57 and -29 scales were used to categorize severe to moderate depression and anxiety. We computed the sensitivity, specificity, positive predictive and negative predictive values for the categorization. Lastly, we calculated Cohens Kappa values to assess the degree of agreement between legacy instruments and respective PROMIS-57 and-29 domains to assign patients to “depression” and “anxiety” categories. Results Our sample included 150 KTRs (mean (±SD) age was 50 (±17) years, 57% male, 57% white. Based on legacy instruments, 7% had moderate to severe anxiety and 8% had depression while reported PROMIS-57 scores yielded 9% with anxiety and 11% with depression. Calculated anxiety scores showed strong correlations with reported PROMIS-57 (r=0.677, p<0.001), and PROMIS-29 (r=0.760, p<0.001) anxiety scores. Calculated depression scores showed strong correlations with reported PROMIS-57 (r=0.760, p<0.001), and PROMIS-29 (r=0.68, p<0.001) depression scores. The legacy cut offs used for severe to moderate anxiety and depression had high specificity (anxiety=0.95, depression=0.93) and moderate sensitivity (anxiety=0.70, depression=0.58). The Kappa values indicated moderate agreement between GAD-7 categorization of anxiety versus PROMIS-57 (K=0.55) and PROMIS-29 (K=0.56). Similarly, there was moderate agreement between PHQ-9 classification of depression versus PROMIS-57 (K=0.45) and PROMIS-29 (K=0.52). Conclusions The PROMIS-57 and -29 depression and anxiety domains are valid self-report tools that can be used to assess depressive and anxiety symptoms. Furthermore, the shorter questionnaire seems to be a good alternative to reduce questionnaire burden.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.022
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.055
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), 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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Citations1
Published2018
Admission routes1
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