Outcome measures in trauma and orthopaedics: a guide to evaluating your practice

Improvements in health-related quality of life (HRQoL) are key in orthopaedic surgery, however appropriate, timely and meaningful measurement of HRQoL can be challenging. This paper provides an overview of the different types of commonly used outcome measures along with their characteristics and key properties used in trauma and orthopaedics. Orthopaedic surgeons are provided a basis to select the most appropriate measure to evaluate their clinical practice.     

Predicting confrontation naming item difficulty

Background: Item response theory (IRT; Lord & Novick, 1968) is a psychometric framework that can be used to model the likelihood that an individual will respond correctly to an item. Using archival data (Mirman et al., 2010), Fergadiotis, Kellough, and Hula (2015) estimated difficulty parameters for the Philadelphia Naming Test (PNT; Roach, Schwartz, Martin, Grewal, & Brecher, 1996) using the 1-parameter logistic IRT model. Although the use of IRT in test development is advantageous, its reliance on sample sizes exceeding 200 participants make it difficult to implement in aphasiology. Therefore, alternate means of estimating the item difficulty of confrontation naming test items warrant investigation. In a preliminary study aimed at automatic item calibration, Swiderski, Fergadiotis, and Hula (2016) regressed the difficulty parameters from the PNT on word length, age of acquisition (Kuperman, Stadthagen-Gonzalez, & Brysbaert, 2012), lexical frequency as quantified by the Log10CD index (Brysbaert & New, 2009), and naming latency (Székely et al., 2003). Although the model's predictive utility was high, a substantial proportion (20%) of the response time data were missing. Further, only 39% of the picture stimuli from Székely and colleagues (2003) were identical to those on the PNT. Given that the IRT sample size requirements limit traditional calibration approaches in aphasiology and that the initial attempts in predicting IRT difficulty parameters in our pilot study were based on incomplete response time data this study has two specific aims.

Aims: To estimate naming latencies for the 175 items on the PNT, and assess the utility of psycholinguistic variables and naming latencies for predicting item difficulty.

Methods and Procedures: Using a speeded picture naming task we estimated mean naming latencies for the 175 items of the PNT in 44 cognitively healthy adults. We then re-estimated the model reported by Swiderski et al (2016) with the new naming latency data.

Outcomes and Results: The predictor variables described above accounted for a substantial proportion of the variance in the item difficulty parameters (Adj. R² = .692).

Conclusions: In this study we demonstrated that word length, age of acquisition, lexical frequency, and naming latency from neurotypical young adults usefully predict picture naming item difficulty in people with aphasia. These variables are readily available or easily obtained and the regression model reported may be useful for estimating confrontation naming item difficulty without the need for collection of response data from large samples of people with aphasia.     

Parkinson’s disease and the Stroop color word test: processing speed and interference algorithms

Objective: Processing speed alters the traditional Stroop calculations of interference. Consequently, alternative algorithms for calculating Stroop interference have been introduced to control for processing speed, and have done so in a multiple sclerosis sample. This study examined how these processing speed correction algorithms change interference scores for individuals with idiopathic Parkinson’s disease (PD, n = 58) and non-PD peers (n = 68). Method: Linear regressions controlling for demographics predicted group (PD vs. non-PD) differences for Jensen’s, Golden’s, relative, ratio, and residualized interference scores. To examine convergent and divergent validity, interference scores were correlated with standardized measures of processing speed and executive function. Results: PD–non-PD differences were found for Jensen’s interference score, but not Golden’s score, or the relative, ratio, and residualized interference scores. Jensen’s score correlated significantly with standardized processing speed but not executive function measures. Relative, ratio, and residualized scores correlated with executive function but not processing speed measures. Golden’s score did not correlate with any other standardized measures. Conclusions: The relative, ratio, and residualized scores were comparable for measuring Stroop interference in processing speed-impaired populations. Overall, the ratio interference score may be the most useful calculation method to control for processing speed in this population.     

Validation of the Geriatric Oral Health Assessment Index in complete denture wearers

To perform a validation of the Geriatric Oral Health Assessment Index (GOHAI) for complete denture wearers and present a proposal for estimation of perceived oral health. This is a cross‐sectional study with non‐probabilistic sampling. A total of 211 subjects with a mean age of 62·5 (s.d. = 11·4) years participated, being 169 female. The GOHAI was applied in a personal interview. The construct/convergent/discriminant validity was tested using structural equation modelling. Confirmatory factor analysis was used to verify the fit of three proposals of the GOHAI: three‐factor, one‐factor and second‐order hierarchical models. The stability of the models was evaluated in independent samples. The three‐factor model presented an inadequate fit, and items 3, 4 and 9 were removed. The new structure presented an acceptable fit and strong invariance in independent samples. The convergent, discriminant validity and internal consistency were below adequate. The one‐factor model presented an adequate fit to the sample. Convergent validity was compromised. A strong invariance of the one‐factor model was observed. To calculate the overall scores of the GOHAI factors (three‐factor model) or of the oral health perception (one‐factor model), a matrix of regression weights for each item in the model was presented as a suggestion. We found an adequate fit of the both structures of the GOHAI for denture wearers, but the three‐factor structure was more parsimonious. We suggested considering the weights of the regression model to calculate the overall score of perceived oral health or of its factors in different samples.