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Record W4414228674 · doi:10.2106/jbjs.24.01348

Psychometric Properties and Feasibility of PROMIS Computerized Adaptive Tests Compared with Disease-Specific Measures in Knee Arthroplasty

2025· article· en· W4414228674 on OpenAlexaboutno aff
Olivier Dhollander, Leo D. Roorda, Seydou Diarra, Ignace Ghijselings, Alex Demurie, Caroline B. Terwee, Olivier Cornu, Hans Van den Wyngaert

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsComputerized adaptive testingArthroplastyMeasure (data warehouse)Test (biology)Minimal clinically important difference

Abstract

fetched live from OpenAlex

BACKGROUND: The efficient assessment of health outcomes in knee arthroplasty may benefit from universally applicable Patient-Reported Outcomes Measurement Information System computerized adaptive tests (PROMIS CATs), rather than disease-specific measures. This study aimed to evaluate and compare some psychometric properties and the feasibility of various PROMIS CATs (Pain Interference [PROMIS-PI-CAT, v1.1], Physical Function [PROMIS-PF-CAT, v2.0], Mobility [PROMIS-Mob-CAT, v2.0], Ability to Participate in Social Roles and Activities [PROMIS-AS-CAT, v2.0], and Satisfaction with Social Roles and Activities [PROMIS-SS-CAT, v2.0]), with the Knee Injury and Osteoarthritis Outcome Score (KOOS) scales, including the KOOS Physical Function Shortform [KOOS-PS] and KOOS for Joint Replacement [KOOS-JR], and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scales. METHODS: Patients (n = 193; mean age [and standard deviation], 64.4 ± 10.1 years; 56% female; mean body mass index, 29.6 ± 5.2 kg/m 2 ) undergoing unilateral or bilateral primary or revision knee arthroplasty at AZ Alma (Eeklo, Belgium) completed the measures 6 weeks before and 6 weeks and 3, 6, and 12 months after surgery. The study evaluated precision (standard error as a percentage of scale range [SE%]), responsiveness (hypothesized correlations and standardized response mean [SRM]), floor and ceiling effects (percentage with the worst and the best scores), and feasibility (completion time and number of items). RESULTS: The PROMIS-PI-CAT and PROMIS-PF-CAT showed better precision at baseline compared with corresponding KOOS/WOMAC scales (SE%, 4.6 versus 7.1/9.3 and 3.6 versus 4.4/4.4), but less precision at 12 months of follow-up (SE%, 6.8 versus 4.8/5.5 and 3.6 versus 3.0/3.0). All PROMIS CATs had good responsiveness (75% to 100% of hypotheses not rejected; SRMs at 12 months: PROMIS-PI-CAT = -1.35 versus KOOS Pain = 1.78 and WOMAC Pain = -1.59; PROMIS-PF-CAT = 1.14 versus KOOS-ADL/WOMAC-PF = 1.43/-1.44; PROMIS-AS-CAT = 0.93 and PROMIS-SS-CAT = 0.93). The PROMIS-PF-CAT did not show ceiling effects at 12 months, unlike the KOOS-ADL/WOMAC-PF (17.5%). PROMIS CATs were more feasible at baseline and follow-ups compared with KOOS and WOMAC scales. CONCLUSIONS: PROMIS-CATs effectively assess health outcomes in knee arthroplasty patients, showing strong psychometric properties and favorable feasibility, supporting their role in value-based health care. LEVEL OF EVIDENCE: Prognostic Level II . See Instructions for Authors for a complete description of levels of evidence.

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)0.036
metaresearch head score (Gemma)0.094
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 score0.036
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.094
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
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 head0.089
GPT teacher head0.265
Teacher spread0.177 · 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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Citations3
Published2025
Admission routes1
Has abstractyes

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