Delawan, Maliya

OBJECTIVE Surgical site infection (SSI) is a leading cause of morbidity following posterior spine surgery. The optimal choice of skin closure technique depending on patient- and procedure-specific characteristics remains to be elucidated. METHODS The Neurosurgery Enterprise Registry (NER) at Mayo Clinic was queried for patients who underwent posterior spine surgery from 2017 to 2023. Propensity matching was performed to match patients on the basis of the development of SSI and baseline characteristics, including age, sex, race, and comorbidities. Perioperative outcomes were analyzed using data from the NER and medical records. RESULTS A total of 18,842 cases of posterior spine surgery were identified in the NER, and SSI was observed in 154 cases (0.8%). Of these patients, 308 patients were included in the 1:1 propensity score–matched cohort analysis, with 154 patients included in each group. Sutures were used in 224 patients (72.7%) and staples in 84 patients (27.3%) (p < 0.01). Female patients comprised 45.1% of the sutures group and 46.4% of the staples group (p = 0.8). The mean ± SD age was 59.8 ± 15.4 years in the sutures group and 60.8 ± 11.9 years in the staples group (p = 0.6). Fusion was performed in 27.2% of patients in the sutures group and 44% in the staples group (p < 0.01). Multilevel fusion comprised 68.9% of fusion cases in the sutures group and 67.6% in the staples group (p = 0.8). Comorbidities, such as diabetes (p = 0.6), hypertension (p = 0.1), and the use of disease-modifying antirheumatic drugs (DMARDs) (p = 0.5) and immunosuppressants (p = 0.2), did not differ between groups. Univariate analysis performed in the propensity score–matched cohort showed that SSI was observed in 53.6% of patients in the sutures group and 40.5% in the staples group (p = 0.04). No significant differences were noted regarding 30-day (p = 0.3), 90-day (p = 0.2), and 1-year (p = 0.3) readmissions and 30-day (p = 0.7), 90-day (p = 0.8), and 1-year (p = 0.8) reoperations. On multivariable logistic regression, SSI was not significantly associated with the choice of skin closure technique (p = 0.3). CONCLUSIONS After adjustment for spinal fusion, number of fused levels, the use of immunosuppressants, and other risk factors, SSI development was not significantly associated with the use of sutures versus staples following posterior spine surgery in our institution. Additionally, no significant differences were observed regarding baseline characteristics and other perioperative outcomes. The authors’ analysis shows that skin closure technique did not significantly affect SSI rates and other outcomes in these patients.

OBJECTIVE Artificial intelligence (AI) is increasingly capable of academic writing, with large language models such as ChatGPT showing potential to assist or even generate scientific manuscripts. However, concerns remain regarding the quality, reliability, and interpretive capabilities of AI-generated content. The authors’ study aimed to compare the quality of a human-written versus an AI-generated scientific manuscript to evaluate the strengths and limitations of AI in the context of academic publishing. METHODS Two manuscripts were developed using identical titles, abstracts, and tables of a simulated analysis: one authored by a physician with multiple publications, and the other generated by ChatGPT-4o. Three independent and blinded reviewers—two human and one AI—assessed each manuscript across five domains: clarity and readability, coherence and flow, technical accuracy, depth, and conciseness and precision. Each category was scored on a 10-point scale, and qualitative feedback was collected to highlight specific strengths and weaknesses. Additionally, all reviewers were asked to deduce authorship of the manuscripts. RESULTS The AI-generated manuscript scored higher in clarity and readability (mean 9.0 vs 7.2), but lower in technical accuracy (mean 6.3 vs 9.3) and depth (mean 5.5 vs 7.5). However, reviewers noted that the AI version lacked depth, critical analysis, and contextual interpretation. All reviewers accurately identified the authorship of each manuscript and tended to rate the version more favorably when it aligned with their own origin (human or AI); i.e., human reviewers assigned higher scores to the human-written manuscript, while the AI reviewer scored the AI-generated manuscript higher. CONCLUSIONS Although AI models can improve some aspects of scientific writing, particularly clarity and readability, they fall short in critical reasoning and contextual understanding. This reinforces the importance of human authorship and oversight in maintaining the critical analysis and scientific accuracy essential for academic publishing. AI may be used as a complementary tool to support, rather than replace, human-led scientific writing.