Reference/Citation: Hamstra-Wright KL, Bliven KC, Bay C. Risk factors for medial tibial stress syndrome in physically active individuals such as runners and military personnel: a systematic review and meta-analysis. Br J Sports Med. 2015;49(6):362-369.Clinical Question: What factors put physically active individuals at risk to develop medial tibial stress syndrome (MTSS)?Data Sources: The authors performed a literature search of CINAHL, the Cochrane Central Register of Controlled Trials, EMBASE, and MEDLINE from each database's inception to July 2013. The following key words were used together or in combination: armed forces, athlete, conditioning, disorder predictor, exercise, medial tibial stress syndrome, militaries, MTSS, military, military personnel, physically active, predictor, recruit, risk, risk characteristic, risk factor, run, shin pain, shin splints, and vulnerability factor.Study Selection: Studies were included in this systematic review based on the following criteria: original research that (1) investigated risk factors associated with MTSS, (2) compared physically active individuals with and without MTSS, (3) was printed in English, and (4) was accessible in full text in peerreviewed journals.Data Extraction: Two authors independently screened titles or abstracts (or both) of studies to identify inclusion criteria and quality. If the article met the inclusion criteria, the authors extracted demographic information, study design and duration, participant selection, MTSS diagnosis, investigated risk factors, mean difference, clinical importance, effect size, odds ratio, and any other data deemed relevant. After the data extraction was complete, the authors compared findings for accuracy and completeness. When the mean and standard deviation of a particular risk factor were reported 3 or more times, that risk factor was included in the meta-analysis. In addition, the methodologic quality was assessed with an adapted checklist developed by previous researchers. The checklist contained 5 categories: study objective, study population, outcome measurements, assessment of the outcome, and analysis and data presentation. Any disagreement between the authors was discussed and resolved by consensus.Main Results: A total of 165 papers were initially identified, and 21 original research studies were included in this systematic review. More than 100 risk factors were identified in the 21 studies. Continuous data were reported 3 or more times for risk factors of body mass index (BMI), navicular drop, ankle plantarflexion range of motion (ROM), ankle-dorsiflexion ROM, ankleeversion ROM, ankle-inversion ROM, quadriceps angle, hip internal-rotation ROM, and hip external-rotation ROM. As compared with the control group, significant risk factors for developing MTSS identified in the literature were (1) Conclusions: The primary factors that appeared to put a physically active individual at risk for MTSS were increased BMI, increased navicular drop, greater ankle plantar-flexion ROM, and greater hip external-rotation...
Artificial intelligence methods are being applied broadly in society and increasingly in health care and research. Machine learning, a subset of artificial intelligence, represents the study of algorithms that improve automatically with experience. This article provides a basic overview of artificial intelligence, machine learning categories, common applications in the business sphere, advantages and disadvantages of using this technology, and example applications in rehabilitation and other fields for contextual purposes. The study and implementation of machine learning and artificial intelligence can function to improve patient care and represents a burgeoning area of research.
Introduction We provide an updated analysis of data about U.S. Physical Medicine and Rehabilitation (PM&R) residency program applicants collected by the National Resident Matching Program (NRMP). Objective Analyze trends within NRMP data for PM&R residency match rates, compare matched to unmatched applicants, and compare PM&R applicants to other medical specialties. Design Secondary analysis of NRMP data. Setting NRMP data set. Participants Residency program applicants who participated in the NRMP Match, 2007 to 2018. Interventions Not applicable. Main Outcome Measures Number of applicants, match rates, difference in characteristics including rank order list (ROL), U.S. Medical Licensing Examination (USMLE) Step 1 and Step 2 Clinical Knowledge (CK) scores, publications, Alpha Omega Alpha (AOA) status, PhD degree, and experiences in research, volunteer, and work. Results Number of applicants and residency positions increased from 2007 to 2018. Length of ROL increased and was longer for matched compared to unmatched applicants, with maximum mean difference of 7.4 in 2016 (95% confidence interval [CI] 5.6‐9.2). Matched U.S. Allopathic Seniors had higher USMLE scores compared to unmatched, with a mean difference of 12.7 for Step 1 (95% CI 8.3‐17.0) and 12.6 (95% CI 8.6‐16.6) for Step 2 CK (P < .001). Number of publications and volunteer experiences were higher for matched U.S. Allopathic Seniors (0.64, 95% CI 0.09‐1.2 and 1.5, 95% CI 0.65‐2.3, respectively). PM&R USMLE Step 1 and 2 CK scores increased at a significantly faster rate than for all other specialties, with estimated rate differences of 0.46 (95% CI 0.21‐0.71) and 0.69 (95% CI 0.45‐0.93) points per year, respectively. Conclusions PM&R residency has become more competitive. USMLE Step 1 and 2 CK scores have outpaced the inflation of scores in other specialties. ROL length has increased, suggesting more ranked programs to successfully match. These analyses update our knowledge about PM&R residency applicants and suggest surrogate markers for a successful match.
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