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Journal article

Computerized adaptive testing for the Oxford Hip, Knee, Shoulder, and Elbow scores

Abstract:
AIMS: The aim of this study was to develop and evaluate machine-learning-based computerized adaptive tests (CATs) for the Oxford Hip Score (OHS), Oxford Knee Score (OKS), Oxford Shoulder Score (OSS), and the Oxford Elbow Score (OES) and its subscales. METHODS: We developed CAT algorithms for the OHS, OKS, OSS, overall OES, and each of the OES subscales, using responses to the full-length questionnaires and a machine-learning technique called regression tree learning. The algorithms were evaluated through a series of simulation studies, in which they aimed to predict respondents' full-length questionnaire scores from only a selection of their item responses. In each case, the total number of items used by the CAT algorithm was recorded and CAT scores were compared to full-length questionnaire scores by mean, SD, score distribution plots, Pearson's correlation coefficient, intraclass correlation (ICC), and the Bland-Altman method. Differences between CAT scores and full-length questionnaire scores were contextualized through comparison to the instruments' minimal clinically important difference (MCID). RESULTS: The CAT algorithms accurately estimated 12-item questionnaire scores from between four and nine items. Scores followed a very similar distribution between CAT and full-length assessments, with the mean score difference ranging from 0.03 to 0.26 out of 48 points. Pearson's correlation coefficient and ICC were 0.98 for each 12-item scale and 0.95 or higher for the OES subscales. In over 95% of cases, a patient's CAT score was within five points of the full-length questionnaire score for each 12-item questionnaire. CONCLUSION: 2022;3(10):786-794.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1302/2633-1462.310.bjo-2022-0073.r1

Authors

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-1428-5751
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Role:
Author
ORCID:
0000-0002-6037-4069
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Institution:
University of Oxford
Division:
MSD
Department:
Nuffield Department of Population Health
Role:
Author
ORCID:
0000-0002-2358-1333
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Institution:
University of Oxford
Division:
MSD
Department:
Nuffield Department of Population Health
Role:
Author
ORCID:
0000-0002-3692-0779
More by this author
Role:
Author
ORCID:
0000-0001-5197-9413


Publisher:
British Editorial Society of Bone and Joint Surgery
Journal:
Bone & Joint Open More from this journal
Volume:
3
Issue:
10
Pages:
786-794
Publication date:
2022-10-10
DOI:
EISSN:
2633-1462
ISSN:
2633-1462


Language:
English
Keywords:
Pubs id:
1282712
Local pid:
pubs:1282712
Source identifiers:
W4304758062
Deposit date:
2026-04-29
ARK identifier:
This ORA record was generated from metadata provided by an external service. It has not been edited by the ORA Team.

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