The Journal of hand surgery, European volume · 2026
A shorter, algorithm-based Michigan Hand Outcomes questionnaire remains valid for outcome assessment in hand patients.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Mathis Kei H, Peters Sebastiaan T, Chung Kevin C, Herren Daniel B, Selles Ruud W, Slijper Harm P, Marks Miriam, Wouters Robbert M
Аннотация
INTRODUCTION: The Michigan Hand Outcomes Questionnaire is widely used to assess hand function and treatment outcomes. This study aimed to develop a shorter, more patient-friendly version of the original 37-item tool while preserving its psychometric validity.
METHODS: Based on two sets of prospectively collected, complete questionnaire data from patients treated for single hand conditions in the Netherlands (n > 72,000) and Switzerland (n = 623), we used Chi-squared Automated Interaction Detection methods to develop and test decision trees that would reliably predict both questionnaire subdomain scores and the total score using fewer items. The Dutch dataset was split into two parts, one to build the model and the other for validation, and the Swiss data were used to further test the final model. The performance of the shortened questionnaire was examined using intraclass correlation coefficients and Bland-Altman analyses.
RESULTS: The number of items for the total score could be reduced from 37 to 16 and even down to four items in one version, while still achieving results very similar to those for the full questionnaire. The predicted scores from the shortened tool matched the original questionnaire scores well with high intraclass correlation coefficients, ranging from 0.91 to 0.98, and low error.
CONCLUSION: We developed and validated a Chi-squared Automated Interaction Detection-based decision tree questionnaire that markedly reduces item count while preserving high psychometric validity. This shortened Michigan Hand Outcomes Questionnaire is openly available and offers a valid and practical alternative to the full questionnaire, reducing the burden for the patient and improving feasibility for clinical practice and research.
LEVEL OF EVIDENCE: II.
METHODS: Based on two sets of prospectively collected, complete questionnaire data from patients treated for single hand conditions in the Netherlands (n > 72,000) and Switzerland (n = 623), we used Chi-squared Automated Interaction Detection methods to develop and test decision trees that would reliably predict both questionnaire subdomain scores and the total score using fewer items. The Dutch dataset was split into two parts, one to build the model and the other for validation, and the Swiss data were used to further test the final model. The performance of the shortened questionnaire was examined using intraclass correlation coefficients and Bland-Altman analyses.
RESULTS: The number of items for the total score could be reduced from 37 to 16 and even down to four items in one version, while still achieving results very similar to those for the full questionnaire. The predicted scores from the shortened tool matched the original questionnaire scores well with high intraclass correlation coefficients, ranging from 0.91 to 0.98, and low error.
CONCLUSION: We developed and validated a Chi-squared Automated Interaction Detection-based decision tree questionnaire that markedly reduces item count while preserving high psychometric validity. This shortened Michigan Hand Outcomes Questionnaire is openly available and offers a valid and practical alternative to the full questionnaire, reducing the burden for the patient and improving feasibility for clinical practice and research.
LEVEL OF EVIDENCE: II.