Background: Cannabinoids are among the psychoactive substances considered as alternatives to opioids for the alleviation of acute pain. We examined whether self-reported marijuana use was associated with decreased use of prescription opioids following traumatic musculoskeletal injury. Methods: Our analysis included 500 patients with a musculoskeletal injury who completed a survey about their marijuana use and were categorized as (1) never a user, (2) a prior user (but not during recovery), or (3) a user during recovery. Patients who used marijuana during recovery indicated whether marijuana helped their pain or reduced opioid use. Prescription opioid use was measured as (1) persistent opioid use, (2) total prescribed opioids, and (3) duration of opioid use. Persistent use was defined as the receipt of at least 1 opioid prescription within 90 days of injury and at least 1 additional prescription between 90 and 180 days. Total prescribed opioids were calculated as the total morphine milligram equivalents (MME) prescribed after injury. Duration of use was the interval between the first and last opioid prescription dates. Results: We found that 39.8% of patients reported never having used marijuana, 46.4% reported prior use but not during recovery, and 13.8% reported using marijuana during recovery. The estimated rate of persistent opioid use ranged from 17.6% to 25.9% and was not associated with marijuana use during recovery. Marijuana use during recovery was associated with increases in both total prescribed opioids (regression coefficient = 343 MME; 95% confidence interval [CI] = 87 to 600 MME; p = 0.029) and duration of use (coefficient = 12.5 days; 95% CI = 3.4 to 21.5 days; p = 0.027) compared with no previous use (never users). Among patients who reported that marijuana decreased their opioid use, marijuana use during recovery was associated with increased total prescribed opioids (p = 0.008) and duration of opioid use (p = 0.013) compared with never users. Conclusions: Our data indicate that self-reported marijuana use during injury recovery was associated with an increased amount and duration of opioid use. This is in contrast to many patients’ perception that the use of marijuana reduces their pain and therefore the amount of opioids used. Level of Evidence: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

➢ Natural language processing with large language models is a subdivision of artificial intelligence (AI) that extracts meaning from text with use of linguistic rules, statistics, and machine learning to generate appropriate text responses. Its utilization in medicine and in the field of orthopaedic surgery is rapidly growing. ➢ Large language models can be utilized in generating scientific manuscript texts of a publishable quality; however, they suffer from AI hallucinations, in which untruths or half-truths are stated with misleading confidence. Their use raises considerable concerns regarding the potential for research misconduct and for hallucinations to insert misinformation into the clinical literature. ➢ Current editorial processes are insufficient for identifying the involvement of large language models in manuscripts. Academic publishing must adapt to encourage safe use of these tools by establishing clear guidelines for their use, which should be adopted across the orthopaedic literature, and by implementing additional steps in the editorial screening process to identify the use of these tools in submitted manuscripts.

Introduction: A common belief is that some narcotic medications have a higher association with prolonged use. We assessed whether the initial opiate type prescribed to postoperative, opiate-naive orthopaedic trauma patients was associated with prolonged opioid use. Methods: We studied 17,961 adult, opiate-naive patients treated for a surgical musculoskeletal injury. Discharge prescription in morphine milligram equivalents (MMEs, a standardized dosing unit that allows for comparison across opioid types) was calculated. Opioid prescribing beyond 90 days after injury was defined as prolonged use. Results: Initial analysis demonstrated a higher likelihood of prolonged use for patients discharged on hydromorphone or morphine versus hydrocodone. However, when we adjusted for discharge MME, only opioid quantity was predictive of prolonged use (P < 0.001). In addition, discharge MME was associated with opioid type (P < 0.01). Discussion: Persistent opiate use was associated with discharge opioid quantity, not the opioid type. These results highlight the importance of calculating equivalence doses when selecting opioid types and considering amount of narcotics prescribed. Level of Evidence: Level III

The emergence of COVID-19 as a viral pandemic in early 2020 resulted in notable changes to the daily practice, workflow, and education of orthopaedic residencies internationally. In particular, social distancing, residency restructuring, and redeployment to other services has increased heterogeneity in schedules and made the in-person gathering of trainees for education increasingly challenging. These changes may last until 2024 based on some mathematical models, resulting in notable disruptions to orthopaedic education, especially for junior residents. Therefore, in this study, we describe how we converted our in-person PGY-1 skills course into a “virtual” boot camp based on validated training modules and existing American Board of Orthopaedic Surgeons guidelines. Lessons learned from the experience and potential areas for improvement in the use of newer technology to teach cognitive knowledge and skills modules are highlighted with the hope that this can be useful to other orthopaedic residency programs, during the pandemic and also beyond.

Carbon fiber offers numerous material benefits including reduced wear, high strength-to-weight ratio, a similar elastic modulus to that of bone, and high biocompatibility. Carbon fiber implants are increasingly used in multiple arenas within orthopaedic surgery, including spine, trauma, arthroplasty, and oncology. In the orthopaedic oncologic population, the radiolucency of carbon fiber facilitates post-operative imaging for tumor surveillance or recurrence, the monitoring of bony healing and union, and radiation mapping and delivery.

Background: The lay public is increasingly using ChatGPT (a large language model) as a source of medical information. Traditional search engines such as Google provide several distinct responses to each search query and indicate the source for each response, but ChatGPT provides responses in paragraph form in prose without providing the sources used, which makes it difficult or impossible to ascertain whether those sources are reliable. One practical method to infer the sources used by ChatGPT is text network analysis. By understanding how ChatGPT uses source information in relation to traditional search engines, physicians and physician organizations can better counsel patients on the use of this new tool. Questions/purposes: (1) In terms of key content words, how similar are ChatGPT and Google Search responses for queries related to topics in orthopaedic surgery? (2) Does the source distribution (academic, governmental, commercial, or form of a scientific manuscript) differ for Google Search responses based on the topic’s level of medical consensus, and how is this reflected in the text similarity between ChatGPT and Google Search responses? (3) Do these results vary between different versions of ChatGPT? Methods: We evaluated three search queries relating to orthopaedic conditions: “What is the cause of carpal tunnel syndrome?,” “What is the cause of tennis elbow?,” and “Platelet-rich plasma for thumb arthritis?” These were selected because of their relatively high, medium, and low consensus in the medical evidence, respectively. Each question was posed to ChatGPT version 3.5 and version 4.0 20 times for a total of 120 responses. Text network analysis using term frequency–inverse document frequency (TF-IDF) was used to compare text similarity between responses from ChatGPT and Google Search. In the field of information retrieval, TF-IDF is a weighted statistical measure of the importance of a key word to a document in a collection of documents. Higher TF-IDF scores indicate greater similarity between two sources. TF-IDF scores are most often used to compare and rank the text similarity of documents. Using this type of text network analysis, text similarity between ChatGPT and Google Search can be determined by calculating and summing the TF-IDF for all keywords in a ChatGPT response and comparing it with each Google search result to assess their text similarity to each other. In this way, text similarity can be used to infer relative content similarity. To answer our first question, we characterized the text similarity between ChatGPT and Google Search responses by finding the TF-IDF scores of the ChatGPT response and each of the 20 Google Search results for each question. Using these scores, we could compare the similarity of each ChatGPT response to the Google Search results. To provide a reference point for interpreting TF-IDF values, we generated randomized text samples with the same term distribution as the Google Search results. By comparing ChatGPT TF-IDF to the random text sample, we could assess whether TF-IDF values were statistically significant from TF-IDF values obtained by random chance, and it allowed us to test whether text similarity was an appropriate quantitative statistical measure of relative content similarity. To answer our second question, we classified the Google Search results to better understand sourcing. Google Search provides 20 or more distinct sources of information, but ChatGPT gives only a single prose paragraph in response to each query. So, to answer this question, we used TF-IDF to ascertain whether the ChatGPT response was principally driven by one of four source categories: academic, government, commercial, or material that took the form of a scientific manuscript but was not peer-reviewed or indexed on a government site (such as PubMed). We then compared the TF-IDF similarity between ChatGPT responses and the source category. To answer our third research question, we repeated both analyses and compared the results when using ChatGPT 3.5 versus ChatGPT 4.0. Results: The ChatGPT response was dominated by the top Google Search result. For example, for carpal tunnel syndrome, the top result was an academic website with a mean TF-IDF of 7.2. A similar result was observed for the other search topics. To provide a reference point for interpreting TF-IDF values, a randomly generated sample of text compared with Google Search would have a mean TF-IDF of 2.7 ± 1.9, controlling for text length and keyword distribution. The observed TF-IDF distribution was higher for ChatGPT responses than for random text samples, supporting the claim that keyword text similarity is a measure of relative content similarity. When comparing source distribution, the ChatGPT response was most similar to the most common source category from Google Search. For the subject where there was strong consensus (carpal tunnel syndrome), the ChatGPT response was most similar to high-quality academic sources rather than lower-quality commercial sources (TF-IDF 8.6 versus 2.2). For topics with low consensus, the ChatGPT response paralleled lower-quality commercial websites compared with higher-quality academic websites (TF-IDF 14.6 versus 0.2). ChatGPT 4.0 had higher text similarity to Google Search results than ChatGPT 3.5 (mean increase in TF-IDF similarity of 0.80 to 0.91; p < 0.001). The ChatGPT 4.0 response was still dominated by the top Google Search result and reflected the most common search category for all search topics. Conclusion: ChatGPT responses are similar to individual Google Search results for queries related to orthopaedic surgery, but the distribution of source information can vary substantially based on the relative level of consensus on a topic. For example, for carpal tunnel syndrome, where there is widely accepted medical consensus, ChatGPT responses had higher similarity to academic sources and therefore...