A 74-year-old postmenopausal woman visited our gynecology clinic complaining of a palpable abdominal mass. Physical and radiological evaluation indicated that the mass exhibited features of a left ovarian neoplasm showing heterogeneous enhancement. Surgical resection was performed to confirm this suspicion. During surgery, a mass was observed only in the left ovary with no invasive growth, but adhesions to the surrounding peritoneum were seen. Given the patient's age, large mass size, and accompanying uterine myoma and right ovarian cyst, total abdominal hysterectomy with bilateral salpingo-oophorectomy was performed. The final pathologic diagnosis was dedifferentiated liposarcoma. The liposarcoma was suspected to originate from retroperitoneal adipose tissue rather than the ovary. Radiotherapy was planned if a gross lesion indicating recurrence followed 6 months later. This case required a considerable multi-disciplinary approach for diagnosis and treatment because of its ambiguous clinical and radiological findings.
Aged ; Diagnosis ; Female ; Gynecology ; Humans ; Hysterectomy ; Intra-Abdominal Fat ; Leiomyoma ; Liposarcoma* ; Ovarian Cysts ; Ovarian Neoplasms ; Ovary ; Peritoneum ; Radiotherapy ; Recurrence ; Retroperitoneal Neoplasms

Background: Injuries pose a significant global health challenge due to their high incidence and mortality rates. Although injury surveillance is essential for prevention, it is resource-intensive.This study aimed to develop and validate locally deployable large language models (LLMs) to extract core injury-related information from Emergency Department (ED) clinical notes. Methods: We conducted a diagnostic study using retrospectively collected data from January 2014 to December 2020 from two urban academic tertiary hospitals. One served as the derivation cohort and the other as the external test cohort. Adult patients presenting to the ED with injury-related complaints were included. Primary outcomes included classification accuracies for information extraction tasks related to injury mechanism, place of occurrence, activity, intent, and severity. We fine-tuned a single generalizable Llama-2 model and five distinct Bidirectional Encoder Representations from Transformers (BERT) models for each task to extract information from initial ED physician notes. The Llama-2 model was able to perform different tasks by modifying the instruction prompt. Data recorded in injury registries provided the gold standard labels. Model performance was assessed using accuracy and macro-average F1 scores. Results: The derivation and external test cohorts comprised 36,346 and 32,232 patients, respectively. In the derivation cohort’s test set, the Llama-2 model achieved accuracies (95% confidence intervals) of 0.899 (0.889–0.909) for injury mechanism, 0.774 (0.760–0.789) for place of occurrence, 0.679 (0.665–0.694) for activity, 0.972 (0.967–0.977) for intent, and 0.935 (0.926–0.943) for severity. The Llama-2 model outperformed the BERT models in accuracy and macro-average F1 scores across all tasks in both cohorts. Imposing constraints on the Llama-2 model to avoid uncertain predictions further improved its accuracy. Conclusion Locally deployable LLMs, trained to extract core injury-related information from free-text ED clinical notes, demonstrated good performance. Generative LLMs can serve as versatile solutions for various injury-related information extraction tasks.

Background: Injuries pose a significant global health challenge due to their high incidence and mortality rates. Although injury surveillance is essential for prevention, it is resource-intensive.This study aimed to develop and validate locally deployable large language models (LLMs) to extract core injury-related information from Emergency Department (ED) clinical notes. Methods: We conducted a diagnostic study using retrospectively collected data from January 2014 to December 2020 from two urban academic tertiary hospitals. One served as the derivation cohort and the other as the external test cohort. Adult patients presenting to the ED with injury-related complaints were included. Primary outcomes included classification accuracies for information extraction tasks related to injury mechanism, place of occurrence, activity, intent, and severity. We fine-tuned a single generalizable Llama-2 model and five distinct Bidirectional Encoder Representations from Transformers (BERT) models for each task to extract information from initial ED physician notes. The Llama-2 model was able to perform different tasks by modifying the instruction prompt. Data recorded in injury registries provided the gold standard labels. Model performance was assessed using accuracy and macro-average F1 scores. Results: The derivation and external test cohorts comprised 36,346 and 32,232 patients, respectively. In the derivation cohort’s test set, the Llama-2 model achieved accuracies (95% confidence intervals) of 0.899 (0.889–0.909) for injury mechanism, 0.774 (0.760–0.789) for place of occurrence, 0.679 (0.665–0.694) for activity, 0.972 (0.967–0.977) for intent, and 0.935 (0.926–0.943) for severity. The Llama-2 model outperformed the BERT models in accuracy and macro-average F1 scores across all tasks in both cohorts. Imposing constraints on the Llama-2 model to avoid uncertain predictions further improved its accuracy. Conclusion Locally deployable LLMs, trained to extract core injury-related information from free-text ED clinical notes, demonstrated good performance. Generative LLMs can serve as versatile solutions for various injury-related information extraction tasks.

Background: Injuries pose a significant global health challenge due to their high incidence and mortality rates. Although injury surveillance is essential for prevention, it is resource-intensive.This study aimed to develop and validate locally deployable large language models (LLMs) to extract core injury-related information from Emergency Department (ED) clinical notes. Methods: We conducted a diagnostic study using retrospectively collected data from January 2014 to December 2020 from two urban academic tertiary hospitals. One served as the derivation cohort and the other as the external test cohort. Adult patients presenting to the ED with injury-related complaints were included. Primary outcomes included classification accuracies for information extraction tasks related to injury mechanism, place of occurrence, activity, intent, and severity. We fine-tuned a single generalizable Llama-2 model and five distinct Bidirectional Encoder Representations from Transformers (BERT) models for each task to extract information from initial ED physician notes. The Llama-2 model was able to perform different tasks by modifying the instruction prompt. Data recorded in injury registries provided the gold standard labels. Model performance was assessed using accuracy and macro-average F1 scores. Results: The derivation and external test cohorts comprised 36,346 and 32,232 patients, respectively. In the derivation cohort’s test set, the Llama-2 model achieved accuracies (95% confidence intervals) of 0.899 (0.889–0.909) for injury mechanism, 0.774 (0.760–0.789) for place of occurrence, 0.679 (0.665–0.694) for activity, 0.972 (0.967–0.977) for intent, and 0.935 (0.926–0.943) for severity. The Llama-2 model outperformed the BERT models in accuracy and macro-average F1 scores across all tasks in both cohorts. Imposing constraints on the Llama-2 model to avoid uncertain predictions further improved its accuracy. Conclusion Locally deployable LLMs, trained to extract core injury-related information from free-text ED clinical notes, demonstrated good performance. Generative LLMs can serve as versatile solutions for various injury-related information extraction tasks.

Background: Injuries pose a significant global health challenge due to their high incidence and mortality rates. Although injury surveillance is essential for prevention, it is resource-intensive.This study aimed to develop and validate locally deployable large language models (LLMs) to extract core injury-related information from Emergency Department (ED) clinical notes. Methods: We conducted a diagnostic study using retrospectively collected data from January 2014 to December 2020 from two urban academic tertiary hospitals. One served as the derivation cohort and the other as the external test cohort. Adult patients presenting to the ED with injury-related complaints were included. Primary outcomes included classification accuracies for information extraction tasks related to injury mechanism, place of occurrence, activity, intent, and severity. We fine-tuned a single generalizable Llama-2 model and five distinct Bidirectional Encoder Representations from Transformers (BERT) models for each task to extract information from initial ED physician notes. The Llama-2 model was able to perform different tasks by modifying the instruction prompt. Data recorded in injury registries provided the gold standard labels. Model performance was assessed using accuracy and macro-average F1 scores. Results: The derivation and external test cohorts comprised 36,346 and 32,232 patients, respectively. In the derivation cohort’s test set, the Llama-2 model achieved accuracies (95% confidence intervals) of 0.899 (0.889–0.909) for injury mechanism, 0.774 (0.760–0.789) for place of occurrence, 0.679 (0.665–0.694) for activity, 0.972 (0.967–0.977) for intent, and 0.935 (0.926–0.943) for severity. The Llama-2 model outperformed the BERT models in accuracy and macro-average F1 scores across all tasks in both cohorts. Imposing constraints on the Llama-2 model to avoid uncertain predictions further improved its accuracy. Conclusion Locally deployable LLMs, trained to extract core injury-related information from free-text ED clinical notes, demonstrated good performance. Generative LLMs can serve as versatile solutions for various injury-related information extraction tasks.

PURPOSE: This research aims at developing a PC-based spinal screw insertion simulation program and rapid prototyping spine models for correct placement of spinal screws. MATERIALS AND METHODS: We developed a surgical simulator on top of a 3-D medical imaging system V-worksTM (Cybermed, Inc.) and used Z-402 (Z Corporation) models made of hardened starch. RESULTS: The first phase is training surgeons using the simulation software. The trainees could simulate the insertion of spinal screws using the PC-based software. The second phase is a planning software to determine the ideal entry point and insertion angle using the multiplanar reconstruction images of spine CT. Finally, a rapid prototyping model of which the size is identical to the actual bone is produced for simulation surgery prior to the actual one. CONCLUSION: The system provides a tool for educating and training the beginners of spinal screw insertion, and also a pre-surgical simulation environment for planning the actual insertion surgery.
Diagnostic Imaging ; Spine ; Starch

PURPOSE: This research aims at developing a simulation system for training of the correct placement of isometric points in arthroscopic reconstruction of anterior cruciate ligaments, using personal computer-based software and rapid prototyping knee models. MATERIALS AND METHODS: CT scan images of the knee joints of thirteen patients were used. Simulation software was developed on V-works(Clinic3D Inc.), a three-dimensional medical imaging system. Rapid prototyping models were made of hardened starch with a 0.178 mm slice thickness. RESULTS: In the first phase, trainee surgeons can study the positions of the bony attachments of healthy anterior cruciate ligaments, and compare their multiplanar reformatting images and a three-dimensional computer model of the bones. In the second phase, trainee sur-geons can place isometric points on the three-dimensional computer models and compare the results with the points set by a supervis-ing surgeon. Finally, rapid prototyping models, which are almost identical to the actual bones, are produced to allow the trainees to observe the isometric points marked on the models. CONCLUSION: Our system can provide a patient-specific simulation environment for beginners at arthroscopic anterior cruciate ligament reconstruction. It can be used as an educational and training tool for locating the isometric point of the anterior cruciate ligament during an operation.
Anterior Cruciate Ligament Reconstruction ; Anterior Cruciate Ligament* ; Computer Simulation* ; Diagnostic Imaging ; Humans ; Knee ; Knee Joint ; Starch ; Tomography, X-Ray Computed

PURPOSE: The purpose of this study was to develop a personal computer-based method to facilitate the evaluation of pedicle screw position on computed tomography (CT) scan images and to assess its diagnostic value. MATERIAL AND METHOD: A personal computer-based method was developed using the CT images of 17 patients having a total of 84 pedicle screws. Images with a window range of -2, 000 to +3, 000 were inverted; a multiplanar reconstruction viewer was then produced to create these images in the sagittal and coronal planes. Finally, lines circumscribing the threaded portion of screws were drawn on the images. For CT images of thirty-two pedicle screws placed in the lumbar vertebrae of four pigs, screw locations were evaluated by 6 orthopaedic surgeons by our method and by conventional bone window setting. The diagnostic values of the two methods were calculated and compared. RESULT: Our method significantly improved the specificity (from 82% to 94%, p=0.007), the positive predictive value (from 79% to 92%, p=0.015), and inter-observer agreement (from 0.61 to 0.78, p<0.001) in terms of identifying misplaced screws. CONCLUSION: The described method improves the diagnostic accuracy and inter-observer reliability for the identification of misplaced pedicle screws on CT scan images.
Humans ; Lumbar Vertebrae ; Sensitivity and Specificity ; Swine ; Tomography, X-Ray Computed

Background: In the emergency department (ED), adequate pain control is essential for managing patients; however, children with pain are known to receive less analgesia than adults with pain. We introduce the Pain Passport to improve pain management in paediatric patients with suspected fractures in the ED. Methods: This was a before-and-after study. We reviewed the medical records of paediatric patients who were primarily diagnosed with fractures from May to August 2015. After the introduction of the Pain Passport, eligible children were enrolled from May to August 2016. Demographics, analgesic administration rates, time intervals between ED arrival and analgesic administration, and satisfaction scores were obtained. We compared the analgesic prescription rate between the two periods using multiple logistic regression. Results: A total of 58 patients were analysed. The baseline characteristics of subjects during the two periods were not significantly different. Before the introduction of the Pain Passport, 9 children (31.0%) were given analgesics, while after the introduction of the Pain Passport, a significantly higher percentage of patients (24/29, 82.8%) were treated with analgesics (P < 0.001). The median administration times were 112 (interquartile range [IQR], 64-150) minutes in the pre-intervention period and 24 (IQR, 20-74) minutes in the post-intervention period. The median satisfaction score for the post-intervention period was 4 (IQR, 3-5). The adjusted odds ratio for providing analgesics in the post-intervention period was 25.91 (95% confidence interval, 4.36-154.02). Conclusions Patient-centred pain scoring with the Pain Passport improved pain management in patients with suspected fractures in the paediatric ED.

Microbial colonization of the infant gut is unstable and shows a wide range of diversity between individuals. Gut microbiota play an important role in the development of the immune system, and an imbalance in these organisms can affect health, including an increased risk of allergic diseases. Microbial colonization of young infants is affected by the delivery mode at birth and the consequent alterations of gut microbiota in early life affect the development of allergic diseases. We investigated the effects of the delivery mode on the temporal dynamics of gut microbiota in healthy Korean infants. Fecal samples were collected at 1-3 days, 1 month, and 6 months after birth in six healthy infants. Microbiota were characterized by 16S rRNA shotgun sequencing. At the first and third days of life, infants born by vaginal delivery showed a higher richness and diversity of gut microbiota compared with those born by cesarean section. However, these differences disappeared with age. The Bacteroides genus and Bacteroidetes phylum were abundant in infants born by vaginal delivery, whereas Bacilli and Clostridium g4 were increased in infants born by cesarean section. The Firmicutes phylum and Bacteroides genus showed convergent dynamics with age. This study demonstrated the effect of delivery mode on the dynamics of gut microbiota profiles in healthy Korean infants.
Bacteroides ; Bacteroidetes ; Cesarean Section ; Clostridium ; Colon ; Female ; Firmicutes ; Gastrointestinal Microbiome* ; Humans ; Immune System ; Infant* ; Microbiota ; Parturition ; Pregnancy