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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(Preview, Version of record, pdf, 860.0KB, Terms of use)
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- Publisher copy:
- 10.1302/2633-1462.310.bjo-2022-0073.r1
Authors
- 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:
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2633-1462
- Language:
-
English
- Keywords:
- Pubs id:
-
1282712
- Local pid:
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pubs:1282712
- Source identifiers:
-
W4304758062
- Deposit date:
-
2026-04-29
- ARK identifier:
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- Copyright date:
- 2022
- Licence:
- Other
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