Methods: Retrospective study of 120 patients (223 fusion levels) who underwent ACDF between 2016 and 2021. Spacer types included structural allografts, PEEK, titanium, and ceramic cages. Radiographic measurements of subsidence were obtained from immediate (≤8 weeks) and long-term (≥6 months) postoperative lateral cervical radiographs. Multivariable linear regression was used to assess the association between spacer type and subsidence, adjusting for patient demographics, surgical levels, smoking history, and osteopenia. Results: The mean age of patients was 53.6±10.9 years and 41.7% were male; 47.5% had a smoking history and 20.8% had osteopenia. There were 38 one-level (31.7%), 61 two-level (50.8%), and 21 three-level fusions (17.5%). Spacer distribution included 62 structural allografts (51.7%), 27 PEEK (22.5%), 20 titanium (16.7%), and 11 ceramic (9.2%) cages. On multivariable analysis, PEEK cages were associated with significantly less anterior subsidence (β=−0.972, p <0.001) and posterior subsidence (β=−0.666, p=0.001) compared to allografts, and greater preservation of segmental lordosis (β=1.393, p=0.024). No significant differences in subsidence were found between titanium, ceramic, and allograft spacers. Conclusions PEEK cages showed reduced subsidence and better preservation of cervical lordosis compared to structural allografts, while titanium and ceramic cages did not differ significantly from structural allografts. These results suggest that PEEK cages may help minimize subsidence-related complications and improve outcomes.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Background: For anatomic total arthroscopic repair, cementless humeral fixation has recently gained popularity. However, few studies have compared clinical, radiographic, and patient-reported outcomes between cemented and press-fit humeral fixation, and none have performed follow-up for longer than 5 years. In this study, we compared long-term postoperative outcomes in patients receiving a cemented versus press-fit humeral stem anatomic arthroscopic repair. Methods: This study retrospectively analyzed 169 shoulders that required primary anatomic total shoulder arthroplasty (aTSA). Shoulders were stratified by humeral stem fixation technique: cementation or press-fit. Data were collected pre- and postoperatively. Primary outcome measures included range of motion, patient reported outcomes, and radiographic measures. Results: One hundred thirty-eight cemented humeral stems and 31 press-fit stems were included. Significant improvements in range of motion were seen in all aTSA patients with no significant differences between final cemented and press-fit stems (forward elevation: P=0.12, external rotation: P=0.60, and internal rotation: P=0.77). Patient reported outcome metrics also exhibited sustained improvement through final follow-up. However, at final follow-up, the press-fit stem cohort had significantly better overall scores when compared to the cemented cohort (visual analog score: P=0.04, American Shoulder and Elbow Surgeon Score: P<0.01, Simple Shoulder Test score: P=0.03). Humeral radiolucency was noted in two cemented implants and one press-fit implant. No significant differences in implant survival were observed between the two cohorts (P=0.75). Conclusions In this series, we found that irrespective of humeral fixation technique, aTSA significantly improves shoulder function. However, within this cohort, press-fit stems provided significantly better outcomes than cemented stems in terms of patient reported outcome scores.Level of evidence: III.

Methods: Among the patients who underwent TSA, 119 shoulders were retrospectively analyzed. Preoperative and postoperative clinical outcome data were collected. Linear regression analysis (univariate and multivariate) was conducted to evaluate the associations of clinical outcomes with age. Kaplan-Meier curves and Cox regression analyses were performed to evaluate implant survival. Results: At final follow-up, patients of all ages undergoing aTSA experienced significant and sustained improvements in all primary outcome measures compared with preoperative values. Based on multivariate analysis, age at the time of surgery was a significant predictor of postoperative outcomes. Excellent implant survival was observed over the course of this study, and Cox regression survival analysis indicated age and sex to not be associated with an increased risk of implant failure. Conclusions When controlling for sex and follow-up duration, older age was associated with significantly better patient-reported outcome measures. Despite this difference, we noted no significant effects on range of motion or implant survival.Level of evidence: IV.