Background: Development of multiloculation–septation is a challenging entity in empyema patients. In this study, it is aimed to investigate the success rates of videothoracoscopic deloculation (VATS-D) and intrapleural fibrinolytic (IPFib) application after tube thoracostomy.Methods: The study retrospectively examined the patients diagnosed with empyema with multiloculation and septation between January 2005 and December 2014. Among these patients, the study included those who received VATS-D or IPFib therapy.Results: VATS-D (Group 1) was applied to 54 patients and IPFib (Group 2) was applied to 24 patients. The success of both procedures was evaluated considering the need of decortication in the following periods. In the VATS-D group, 4 (7.4%) patients required decortication via thoracotomy where it was 1 (4.1%) patient (p = 0.577) in the IPFib group. The length of hospital stay was 6.81 ± 2.55 (4–15) days in Group 1 compared to 14.25 ± 6.44 (7–27) days in Group 2 (p <0.001).Conclusions: It was demonstrated that both of the methods applied in the study have high efficacy and are preferable methods based on the general conditions of patients. Additionally, the shorter length of hospital stays in patients received VATS-D was established as a significant parameter.

Background: AI-based chatbots are increasingly used as sources of health information. However, their reliability in delivering accurate and scientifically sound responses to patient questions remains uncertain, especially in chronic diseases such as chronic obstructive pulmonary disease (COPD). This study aims to compare the reliability of ChatGPT-4o and Gemini 2.5 Flash in providing patient-centered medical information on COPD. Methods: A total of 34 common public questions about COPD were submitted to ChatGPT-4o and Gemini 2.5 Flash. Responses were evaluated blindly by four pulmonologists across three domains: accuracy, clarity, and scientific adequacy. The mean scores and word counts were analyzed and compared via nonparametric tests. Results: Gemini 2.5 Flash outperforms ChatGPT-4o in terms of scientific adequacy (mean score: 4.69 ± 0.31 vs. 4.34 ± 0.45, p <0.001). No significant difference was found in accuracy or clarity. The Gemini 2.5 Flash also generated significantly longer responses, particularly in the treatment and prognosis domains ( p <0.001). Both models provided generally acceptable answers, but ChatGPT-4o′s responses were shorter and occasionally less complete. Conclusions: While both models delivered largely accurate and understandable content, Gemini 2.5 Flash tended to produce more detailed responses and received higher scientific adequacy ratings; however, this difference should be interpreted in light of the substantial imbalance in response length. These tools may support patient education however, the findings reflect a comparison between AI systems only and should be interpreted within this scope.

17-year-old primigravida patient presented with chest pain, dyspnea, sore throat and a sensation of swelling in the neck and throat approximately 3 hours after difficult vaginal delivery at home. Breath sounds were equal bilaterally. Physical examination revealed subcutaneous emphysema that expanded from the anterior thorax to the neck. Posteroanterior (PA) chest X-ray showed air in the neck and thoracic computed tomography showed pneumomediastinum. The patient was admitted to our clinic and was started on ampicillin-sulbactam 3 × 1 grams iv, paracetamol 2 × 1000 mg and 3 L/min of oxygen therapy. C-reactive protein (CRP) concentration and erythrocyte sedimentation rate (ESR), which were monitored on a daily basis, showed decline. Repeated chest X-rays did not show any progression. The patient was clinically stable and was discharged from the hospital on the third day. Currently in her third month of follow-up, the patient is stable.

Spontaneous pneumothorax is a potentially life-threatening condition commonly diagnosed using chest radiographs. However, interpreting chest X-rays can be challenging due to anatomical overlap and observer variability. This study aimed to evaluate the diagnostic accuracy of ChatGPT, a large language model (LLM), in detecting pneumothorax on chest radiographs compared to expert thoracic surgeons. In this retrospective study, 220 chest radiographs were assessed. Expert consensus classified 110 cases with pneumothorax and 110 without. The images were uploaded to the GPT-4o model without any clinical information, and ChatGPT was asked to identify the presence or absence of pneumothorax. Diagnostic performance was evaluated by calculating sensitivity, specificity, accuracy, positive and negative predictive values, and area under the receiver operating characteristic curve (AUC). Subgroup analyses were performed based on pneumothorax size. ChatGPT demonstrated an overall diagnostic accuracy of 83.7%, sensitivity of 70.9%, specificity of 96.4%, positive predictive value of 95.1%, and negative predictive value of 76.8%. The AUC was 0.836 (95% CI: 0.780-0.893). Diagnostic performance was higher for large pneumothoraces (AUC: 0.894) compared to small pneumothoraces (AUC: 0.439). Cohen’s kappa coefficient indicated substantial agreement (κ=0.673; 95%CI: 0.575-0.771) with expert evaluations. ChatGPT demonstrates potential in detecting pneumothorax on chest radiographs, particularly in cases of large pneumothorax. However, its limited sensitivity for small pneumothoraces raises significant concerns about its reliability in clinical decision-making. Any use of ChatGPT in diagnostic workflows should be approached with caution, as unverified outputs may lead to inappropriate interventions or under-triaging. Therefore, the model is not suitable as a standalone diagnostic or triage tool. Its potential utility may lie in exploratory or supervised settings where expert oversight is available, but further validation is required before clinical implementation can be considered.

Background: This study aims to provide a comprehensive bibliometric evaluation of the most influential publications on salvage surgery for non-small cell lung cancer from 1991 to 2023, highlighting scientific trends, geographic contributions, journal impact, and major clinical themes. A systematic search was conducted in the Web of Science Core Collection using the terms “salvage surgery” and “non-small cell lung cancer.” Only English, SCI-Expanded research articles reporting surgical intervention were included. Among 302 records, the 20 most cited studies were selected. Publication year, country, journal, citation count, clinical characteristics, surgical details, and oncologic outcomes were extracted and analyzed. Despite increasing clinical interest, no prior study has systematically synthesized both bibliometric trends and clinical outcomes in salvage surgery for NSCLC. Results: The included studies received a total of 508 citations, with Japan contributing the highest number (45%). Seventy-five percent of publications were produced in the last decade, reflecting rising academic interest. The most prominent journals were The Annals of Thoracic Surgery and The Journal of Thoracic and Cardiovascular Surgery . Clinical cohorts were small and typically composed of advanced-stage non-small cell lung cancer patients. Lobectomy was the predominant procedure, while morbidity ranged from 19 to 70% and mortality remained low. Achieving R0 resection, pathological N0 status, and lower disease stage were consistently associated with improved survival. Conclusions: This bibliometric assessment demonstrates that salvage surgery is a feasible and oncologically valuable option for selected non-small cell lung cancer patients following definitive chemoradiotherapy or other non-operative treatments. Despite high morbidity, perioperative mortality is acceptable, and complete resection remains the strongest predictor of long-term survival. Future prospective, multicenter, and standardized studies are required to strengthen the evidence base and refine patient selection.