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OBJECTIVES: In Turkey, conducting research for a dissertation is necessary to obtain a specialist degree, but publication of this research is not mandatory. Previous studies have shown a low rate of publication for dissertation-derived research. The aim of this study was to determine publication rates, factors affecting the transformation of the dissertations into high-quality publications, and bibliometric analysis of published articles in the field of emergency medicine (EM). METHODS: This was a retrospective bibliometric study of EM dissertations submitted between 1998 and 2021 to the National Thesis Center. Research characteristics, publication status, journal characteristics, indexing, citation analysis, and institution characteristics were recorded. Journals indexed in the web of science (WOS) were defined as high-quality journals. A logistic regression was performed to identify factors affecting publication in high-quality journals. RESULTS: A total of 2434 dissertations were included. Of these, 864 (35.5%) were published and 474 (54%) were published in WOS-indexed journals. The most common area of research was trauma (n = 150, 17%), and the most common journal was the American Journal of EM (n = 74, 8%). Prospective data collection (odds ratio [OR] = 2.5, 95% confidence interval [CI] = 1.8-2.5), experimental design (OR = 2, 95%, CI = 1.3-3), university-type residency program (OR = 1.4, 95% CI = 1.02-2.1), and duration between year of graduation and publication (OR = 0.9, 95% CI = 0.84-0.95) were associated with publishing in WOS-indexed journals. CONCLUSION: EM is a relatively successful specialty for publishing dissertation-derived studies. Prospective and experimental research design, graduation from a university-type residency program, and shorter duration between the graduation and publication may increase the chance of publishing in high-quality journals.
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OBJECTIVES: Artificial intelligence companies have been increasing their initiatives recently to improve the results of chatbots, which are software programs that can converse with a human in natural language. The role of chatbots in health care is deemed worthy of research. OpenAI's ChatGPT is a supervised and empowered machine learning-based chatbot. The aim of this study was to determine the performance of ChatGPT in emergency medicine (EM) triage prediction. METHODS: This was a preliminary, cross-sectional study conducted with case scenarios generated by the researchers based on the emergency severity index (ESI) handbook v4 cases. Two independent EM specialists who were experts in the ESI triage scale determined the triage categories for each case. A third independent EM specialist was consulted as arbiter, if necessary. Consensus results for each case scenario were assumed as the reference triage category. Subsequently, each case scenario was queried with ChatGPT and the answer was recorded as the index triage category. Inconsistent classifications between the ChatGPT and reference category were defined as over-triage (false positive) or under-triage (false negative). RESULTS: Fifty case scenarios were assessed in the study. Reliability analysis showed a fair agreement between EM specialists and ChatGPT (Cohen's Kappa: 0.341). Eleven cases (22%) were over triaged and 9 (18%) cases were under triaged by ChatGPT. In 9 cases (18%), ChatGPT reported two consecutive triage categories, one of which matched the expert consensus. It had an overall sensitivity of 57.1% (95% confidence interval [CI]: 34-78.2), specificity of 34.5% (95% CI: 17.9-54.3), positive predictive value (PPV) of 38.7% (95% CI: 21.8-57.8), negative predictive value (NPV) of 52.6 (95% CI: 28.9-75.6), and an F1 score of 0.461. In high acuity cases (ESI-1 and ESI-2), ChatGPT showed a sensitivity of 76.2% (95% CI: 52.8-91.8), specificity of 93.1% (95% CI: 77.2-99.2), PPV of 88.9% (95% CI: 65.3-98.6), NPV of 84.4 (95% CI: 67.2-94.7), and an F1 score of 0.821. The receiver operating characteristic curve showed an area under the curve of 0.846 (95% CI: 0.724-0.969, P < 0.001) for high acuity cases. CONCLUSION: The performance of ChatGPT was best when predicting high acuity cases (ESI-1 and ESI-2). It may be useful when determining the cases requiring critical care. When trained with more medical knowledge, ChatGPT may be more accurate for other triage category predictions.
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Introduction: Scientific publications originating from medical specialty theses are seen as a start to an academic career for clinicians and a criterion to work in academia in Turkey. Aim: To evaluate thoracic surgery theses in the period 2001-2019 in publication and other bibliometric parameters. Material and methods: Our study investigated 319 theses prepared in the thoracic surgery field between January 2001 and December 2019 and registered in the National Thesis Center. We identified and recorded the author's gender, institution, research method, publication status, time, citations, journals' index, and author's order using Google Scholar, Web of Science Basic Search, and Master Journal List. Results: Of the 319 evaluated, 262 theses were from universities, and 57 were in Training and Research Hospitals. Thirty-two studies (10%) were experimental or prospective clinical. The number of published studies (38.5%) in journals was 123 (66 SCI/SCI-E, 8 ESCI, three other international indexes, and 46 national indexes). Sixty (18.8%) authors were women. The mean time to publication was 4.31 ±2.95 years. It was 3.3 years for female researchers (p = 0.029). Experimental/prospective studies in universities were relatively higher. The number of citations in SCI/SCI-E journals was significantly higher (p < 0.001). The time to the publication of experimental/prospective studies was shorter (p = 0.039). Conclusions: The publication rate of thoracic surgery theses was 38.5%. Female researchers published their studies earlier. Articles in SCI/SCI-E journals had a higher number of citations. The time to publication was significantly shorter in experimental/prospective studies. This study is the first in the literature as a bibliometric report of the thoracic surgery thesis.
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Acute pancreatitis is a frequent reason for emergency admission, which has seen its numbers increase over the years. This condition has systemic, local, and vascular complications. A 73-year-old male patient presented to our emergency department complaining of abdominal pain, nausea, and vomiting. During imaging, intraventricular thrombus was discovered, and following completion of diagnostic testing, he was diagnosed with acute pancreatitis. Herein, we present the first case of intraventricular thrombus related to acute pancreatitis prothrombotic process in the literature.