A case of sacrococcygeal chordoma diagnosed by fine needle aspiration is presented. This is a case of a 54-year-old woman who came with coccygeal pain of 5-6 months duration. Aspiration biopsy cytology revealed many nests of cells having abundant bubbly cytoplasm and round to oval variably sized nuclei. The cells had indistinct cytoplasmic borders and many of the cells had cytoplasmic vacuoles. The nuclei had thin regular nuclear membranes, finely granular chromatin and one or two small nucleoli. The cells were generally monotonous, but focally pleomorphic with giant cell formation. Mitotic figures were scanty. The backgroud of the aspirate contained abundant mucinous materal. These findings were typical of those of recorded chordoma cases and the diagnosis was confirmed by a following open biopsy. The patient received 4,000 rads of neutron radiotherapy and has been well till March '88.
Female ; Humans ; Biopsy

Fine needle aspiration cytologic findings of four cases of chondrosarcoma were described. The cases consisted of one primary scapular tumor, two recurrent shoulder masses, and right upper quadrant mass which developed after an A-K amputation for an unknown tumor. The aspirates characteristically revealed cell-rich smears containing clusters and isolated cells having abundant cyanophilic cytoplasm and round to oval or elongated vesicular nuclei. The cytoplasm was occasionally foamy. The nuclei were usually small but prominent. Nuclear atypism and pleomorphism were frequently associated. The last case showed epithelioid sheets of polygonal cells, possibly representing chondroblasts, and a well differentiated chondroid element. The fine needle aspiration could be a good diagnostic tool for primary, recurrent, and metastatic chondrosarcomas.
Neoplasm Metastasis

Fine needle aspiration cytologic findings of four cases of chondrosarcoma were described. The cases consisted of one primary scapular tumor, two recurrent shoulder masses, and right upper quadrant mass which developed after an A-K amputation for an unknown tumor. The aspirates characteristically revealed cell-rich smears containing clusters and isolated cells having abundant cyanophilic cytoplasm and round to oval or elongated vesicular nuclei. The cytoplasm was occasionally foamy. The nuclei were usually small but prominent. Nuclear atypism and pleomorphism were frequently associated. The last case showed epithelioid sheets of polygonal cells, possibly representing chondroblasts, and a well differentiated chondroid element. The fine needle aspiration could be a good diagnostic tool for primary, recurrent, and metastatic chondrosarcomas.
Neoplasm Metastasis

No abstract available.

No abstract available.
Electrocardiography, Ambulatory*

Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality. Methods: This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing. Results: Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700–1.000). Conclusions: The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.

Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality. Methods: This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing. Results: Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700–1.000). Conclusions: The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.

Identifying critically ill patients at risk of cardiac arrest is important because it offers the opportunity for early intervention and increased survival. The aim of this study was to develop a deep learning model to predict critical events, such as cardiopulmonary resuscitation or mortality. Methods: This retrospective observational study was conducted at a tertiary university hospital. All patients younger than 18 years who were admitted to the pediatric intensive care unit from January 2010 to May 2023 were included. The main outcome was prediction performance of the deep learning model at forecasting critical events. Long short-term memory was used as a deep learning algorithm. The five-fold cross validation method was employed for model learning and testing. Results: Among the vital sign measurements collected during the study period, 11,660 measurements were used to develop the model after preprocessing; 1,060 of these data points were measurements that corresponded to critical events. The prediction performance of the model was the area under the receiver operating characteristic curve (95% confidence interval) of 0.988 (0.9751.000), and the area under the precision-recall curve was 0.862 (0.700–1.000). Conclusions: The performance of the developed model at predicting critical events was excellent. However, follow-up research is needed for external validation.