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Record W4396993622 · doi:10.1681/asn.20213210s1632b

Construct Validity of the Patient-Reported Outcomes Measurement Information System (PROMIS®) Profile Summary Scores in Patients with Kidney Failure

2021· article· en· W4396993622 on OpenAlexaff
István Mucsi, Gaauree Chawla, Anqi Chen, Mark JP M. Sanchez, Nathaniel Edwards, John Devin Peipert, Madeline Li, Doris Howell, Susan J. Bartlett, Ron D. Hays

Bibliographic record

VenueJournal of the American Society of Nephrology · 2021
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsPrincess Margaret Cancer CentreMcGill UniversityUniversity Health Network
Fundersnot available
KeywordsPatient-Reported Outcomes Measurement Information SystemConstruct (python library)Construct validityMedicinePsychometricsClinical psychologyComputer scienceComputerized adaptive testing

Abstract

fetched live from OpenAlex

Background: The PROMIS® profiles include a single pain intensity item and 7 multi-item scales (e.g., physical function, fatigue, depression, social participation, etc.). These domains can be summarized into physical (PHS) and mental health summary (MHS) scores. We examine correlations of the PHS and MHS with generic and kidney-disease targeted measures in patients treated with kidney replacement therapies and compare the summary scores between patients on dialysis vs kidney transplant. Methods: Cross-sectional convenience sample of 606 adults. Higher PHS and MHS scores correspond to better health. We estimated correlations of the PHS and MHS with the SF-12 physical (PCS) and mental component score (MCS), the Patient Health Questionnaire (PHQ-9), EQ-5D-5L, KDQOL-36 symptom scores, and serum albumin. The PHS was hypothesized to be strongly associated with other measures of physical health, and the MHS with other measures of mental health. Results: Correlations with the PROMIS PHS and MHS (Table) with legacy health-related quality of life measures were large. The patterns of correlations of the PHS and MHS were consistent with a-priori hypotheses. Patients on dialysis were older (mean[SD] age 64(14) vs 50(15) years), and less likely to be White (32% vs 68%); p<0.01 for all. Kidney transplant recipients reported better health than patients on dialysis: PHS (mean[SD] 47[10] vs 37[9], p<0.001) and MHS (50[9] vs 45[9], p<0.001) and this remained significant in multivariable adjusted (age, sex, ethnicity, marital status, comorbidity, serum albumin and hemoglobin) regression models (coefficient[95% CI] of difference between dialysis and transplant for PH:5.9 [3.8-7.9]; for MH: 3.2 [1.0-5.3]; both p<0.01). Conclusions: These results support the construct validity of PROMIS PHS and MHS scores among patients treated with kidney replacement therapies. PHS and MHS was substantially better among kidney recipients compared to patients on dialysis.TableFootnote: * : higher PHS and MHS indicates better health; higher PHQ-9 score indicates more severe depressive symptoms; the correlation is negative

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.010
metaresearch head score (Gemma)0.030
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 score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.226
Teacher spread0.212 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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