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and Purpose. The purpose of this study was to determine whether physical therapy interventions predicted meaningful short-term improvement in 4 measures of physical health, pain, and function for patients diagnosed with adhesive capsulitis. Participants. Data were examined from 2,370 patients (mean ageϭ55.3 years, SDϭ12.4; 65% female, 35% male) classified into ICD-9 code 726.0 who had completed an episode of outpatient physical therapy. Methods. Principal components factor analysis was used todoi:10.2522/ptj.20080250 pmid:19270045 fatcat:5nsb5xnntffh3ktiy2zq4se6de
more »... alysis was used to define intervention categories from specific treatments applied during the episode of care. A nested logistic regression model was used to identify intervention categories that predicted a 50% or greater change in Physical Component Summary-12 (PCS-12), physical function (PF), bodily pain (BP), and hybrid function (HF) scores. Results. None of the patients achieved a 50% or greater improvement in PCS-12 scores. Improvement in BP scores was more likely in patients who received joint mobility interventions (odds ratioϭ1.35, 95% confidence intervalϭ1.10 -1.65). Improvement in HF scores was more likely in patients who received exercise interventions (odds ratioϭ1.50, 95% confidence intervalϭ1.03-2.17). Use of iontophoresis, phonophoresis, ultrasound, or massage reduced the likelihood of improvement in these 3 outcome measures by 19% to 32%. Limitations. The authors relied on clinician-identified ICD-9 coding for the diagnosis. Impairment measures were not available to support the diagnosis, and some interventions were excluded because of infrequent use by participating therapists. Discussion and Conclusions. These results are consistent with findings from randomized clinical trials that demonstrated the effectiveness of joint mobilization and exercise for patients with adhesive capsulitis. Ultrasound, massage, iontophoresis, and phonophoresis reduced the likelihood of a favorable outcome, which suggests that use of these modalities should be discouraged.
Methods Programs provided data regarding student demographic characteristics, undergraduate grade point average (uGPA), quantitative and verbal Graduate Record Examination scores (qGRE, vGRE), and academic difficulty. Data were analyzed using logistic regression. Rules for predicting risk of academic difficulty were developed. Results A prediction rule that included uGPA, vGRE, qGRE, age, and race or ethnicity was developed from the entire sample. Prediction rules for individual programs showeddoi:10.2522/ptj.20060221 pmid:17609336 fatcat:pp3q64porfe53mmxrukgsatjfy