Background: Autologous osteochondral transplantation (AOT) has been shown to be a viable treatment option for large osteochondral lesions of the talus. However, there are limited data regarding the management of large lesions in an athletic population, notably with regard to return to sport. Our investigation focused on assessing both qualitative and quantitative outcomes in the high-demand athlete with large (>150 mm2) lesions. Hypothesis: AOT is a viable option in athletes with large osteochondral lesions and can allow them to return to sport at their preinjury level. Study Design: Case series; Level of evidence, 4. Methods: The study population was limited to professional and amateur athletes (Tegner score, >6) with a talar osteochondral lesion size of 150 mm2 or greater. The surgical intervention was AOT with a donor site from the lateral femoral condyle. Clinical outcomes at a minimum of 24 months included return to sport, visual analog scale (VAS) for pain score, and Foot and Ankle Outcome Score (FAOS). In addition, graft incorporation was evaluated by magnetic resonance imaging (MRI) using MOCART (magnetic resonance observation of cartilage repair tissue) scores at 12 months after surgery. Results: A total of 38 athletes, including 11 professional athletes, were assessed. The mean follow-up was 45 months. The mean lesion size was 249 mm2. Thirty-three patients returned to sport at their previous level, 4 returned at a lower level compared with preinjury, and 1 did not return to sport (mean return to play, 8.2 months). The VAS improved from 4.53 preoperatively to 0.63 postoperatively ( P = .002). FAOSs improved significantly in all domains ( P < .001). Two patients developed knee donor site pain, and both had 3 osteochondral plugs harvested. Univariant analysis demonstrated no association between preoperative patient or lesion characteristics and ability to return to sport. However, there was a strong correlation between MOCART scores and ability to return to sport. The area under receiver operating characteristic of the MOCART score and return to play was 0.891 ( P = .005), with a MOCART score of 52.50 representing a sensitivity of 0.85 and specificity of 0.80 in determining ability to return to one’s previous level of activity. Conclusion: Our study suggests that AOT is a viable option in the management of large osteochondral talar defects in an athletic population, with favorable return to sport level, patient satisfaction, and FAOS/VAS scores. The ability to return to sport is predicated upon good graft incorporation, and further research is required to optimize this technique. Our data also suggest that patients should be aware of the increased risk of developing knee donor site pain when 3 osteochondral plugs are harvested.
This essay examines how Goodreads users define, discuss, and debate "classic" literature by computationally analyzing and close reading more than 120,000 user reviews. We begin by exploring how crowdsourced tagging systems like those found on Goodreads have influenced the evolution of genre among readers and amateur critics, and we highlight the contemporary value of the "classics" in particular. We identify the most commonly tagged "classic" literary works and find that Goodreads users have curated a vision of literature that is less diverse, in terms of the race and ethnicity of authors, than many U.S. high school and college syllabi. Drawing on computational methods such as topic modeling, we point to some of the forces that influence readers' perceptions, such as schooling and what we call the classic industryindustries that benefit from the reinforcement of works as classics in other mediums and domains like film, television, publishing, and e-commerce (e.g., Goodreads and Amazon). We also highlight themes that users commonly discuss in their reviews (e.g., boring characters) and writing styles that often stand out in them (e.g., conversational and slangy language). Throughout the essay, we make the case that computational methods and internet data, when combined, can help literary critics capture the creative explosion of reader responses and critique algorithmic culture's effects on literary history.What is a classic? This is "not a new question," as T.S. Eliot acknowledged more than seventy-five years ago. 1 More than simply "not new," this question now feels decidedly old, hashed out, and even passé. Perhaps most glaringly outdated is the word "classic." Literary scholars don't often use the term anymore, at least not as a serious label for literature "of the highest rank or importance." 2 In 1991, John Guillory declared that the term classic was "all but retired." 3 The label, according to Guillory, signified not only a "relatively uncritical regard for the great works of Western literature" but the "precritical era of criticism itself." 4 Instead, in academic conversations, the ardent language of the "classics" has largely been displaced by the more critical vocabulary of the "canon," which frames literary significance more carefully as a product of cultural selection.
The speech development of nine children with cleft lip/palate was followed longitudinally from nine months to three years of age. The results indicate speech sound development closer to the non-cleft population than previous studies. Nasal fricatives previously not extensively described in the literature may be an experimental stage of developmental babble, which spontaneously reduce. The study has added to the evidence-base for practice in one cleft unit. It may be useful to channel resources at our centre to children who at nine months may be more at risk, i.e. children with bilateral clefts and known developmental delay.
RIVETER provides a complete easy-to-use pipeline for analyzing verb connotations associated with entities in text corpora. We prepopulate the package with connotation frames of sentiment, power, and agency, which have demonstrated usefulness for capturing social phenomena, such as gender bias, in a broad range of corpora. For decades, lexical frameworks have been foundational tools in computational social science, digital humanities, and natural language processing, facilitating multifaceted analysis of text corpora. But working with verb-centric lexica specifically requires natural language processing skills, reducing their accessibility to other researchers. By organizing the language processing pipeline, providing complete lexicon scores and visualizations for all entities in a corpus, and providing functionality for users to target specific research questions, RIVETER greatly improves the accessibility of verb lexica and can facilitate a broad range of future research.
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