Physiotherapy research international : the journal for researchers and clinicians in physical therapy · 2025
Upper Limb Function 3 Months Post-Stroke: How Accurate Are Physiotherapist Predictions?
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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.
Bastholm Sara Kjær, Brunner Iris Charlotte, Lundquist Camilla Biering
Аннотация
BACKGROUND: A frequent sequela of stroke is upper limb (UL) impairment. Accurate UL function prognosis is crucial for targeted rehabilitation.
OBJECTIVE: To determine the accuracy of physiotherapists' predictions of UL function and investigate whether prediction accuracy is affected by physiotherapists' seniority within rehabilitation and/or their level of education. Physiotherapist predictions were compared with a prediction algorithm.
METHODS: Data from 81 patients were included. Two weeks post-stroke, physiotherapists predicted UL function based on clinical reasoning. ARAT scores (poor, limited, good, or excellent) at 3 months post-stroke served to determine prediction accuracy. Prediction accuracy was calculated as correct classification rate (CCR). Logistic regression was used to explore the effect of seniority and education. McNemar's test was applied to compare physiotherapist predictions to an algorithm applied 2 weeks post-stroke to the same patients.
RESULTS: The overall correct classification rate (CCR) of physiotherapist predictions was 41% (95% CI: 30-51). Predictions were most accurate for the excellent (75%) and poor (71%) categories, but lower for limited (22%) and good (30%). No association was observed between prediction accuracy and physiotherapist seniority or education. There was a tendency, but not a statistically significant superiority, in the prediction accuracy of the algorithm compared to the physiotherapist predictions (Odds ratio 2 [95% CI: 0.96-4.39], McNemar p = 0.0455, exact McNemar p = 0.0652).
TRIAL REGISTRATION: Project number: 628213.
OBJECTIVE: To determine the accuracy of physiotherapists' predictions of UL function and investigate whether prediction accuracy is affected by physiotherapists' seniority within rehabilitation and/or their level of education. Physiotherapist predictions were compared with a prediction algorithm.
METHODS: Data from 81 patients were included. Two weeks post-stroke, physiotherapists predicted UL function based on clinical reasoning. ARAT scores (poor, limited, good, or excellent) at 3 months post-stroke served to determine prediction accuracy. Prediction accuracy was calculated as correct classification rate (CCR). Logistic regression was used to explore the effect of seniority and education. McNemar's test was applied to compare physiotherapist predictions to an algorithm applied 2 weeks post-stroke to the same patients.
RESULTS: The overall correct classification rate (CCR) of physiotherapist predictions was 41% (95% CI: 30-51). Predictions were most accurate for the excellent (75%) and poor (71%) categories, but lower for limited (22%) and good (30%). No association was observed between prediction accuracy and physiotherapist seniority or education. There was a tendency, but not a statistically significant superiority, in the prediction accuracy of the algorithm compared to the physiotherapist predictions (Odds ratio 2 [95% CI: 0.96-4.39], McNemar p = 0.0455, exact McNemar p = 0.0652).
TRIAL REGISTRATION: Project number: 628213.