scholarly journals Telephonic Medical Toxicology Service in a Low- Resource Setting: Setup, Challenges, and Opportunities

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Eveline Hitti ◽  
Tharwat El Zahran ◽  
Hani Hamade ◽  
Brent Morgan ◽  
Ziad Kazzi
Keyword(s):  
Medical Toxicology ◽  
Low Resource ◽  
Resource Setting ◽  
Low Resource Setting ◽  
Challenges And Opportunities

Evaluation of a New Protocol of Insulin Dose Adjustment in a Low-Resource Setting

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 93-LB
Author(s):  
EDDY JEAN BAPTISTE ◽  
PHILIPPE LARCO ◽  
MARIE-NANCY CHARLES LARCO ◽  
JULIA E. VON OETTINGEN ◽  
EDDLYS DUBOIS ◽  
...  
Keyword(s):  
Dose Adjustment ◽  
Insulin Dose ◽  
Low Resource ◽  
Resource Setting ◽  
Low Resource Setting

scholarly journals Right paraduodenal hernia with extensive bowel gangrene treated with staged surgery: a Bogota bag followed by resection in a low-resource setting

2021 ◽  
Vol 14 (4) ◽  
pp. e239250
Author(s):  
Vijay Anand Ismavel ◽  
Moloti Kichu ◽  
David Paul Hechhula ◽  
Rebecca Yanadi
Keyword(s):  
Plastic Material ◽  
Successful Outcome ◽  
Paraduodenal Hernia ◽  
Transparent Plastic ◽  
Low Resource ◽  
Entire Small Bowel ◽  
Resource Setting ◽  
Low Resource Setting ◽  
Right Paraduodenal Hernia ◽  
Bowel Gangrene

We report a case of right paraduodenal hernia with strangulation of almost the entire small bowel at presentation. Since resection of all bowel of doubtful viability would have resulted in too little residual length to sustain life, a Bogota bag was fashioned using transparent plastic material from an urine drainage bag and the patient monitored intensively for 18 hours. At re-laparotomy, clear demarcation lines had formed with adequate length of viable bowel (100 cm) and resection with anastomosis was done with a good outcome on follow-up, 9 months after surgery. Our description of a rare cause of strangulated intestinal obstruction and a novel method of maximising length of viable bowel is reported for its successful outcome in a low-resource setting.

scholarly journals Humanitarian Surgical Missions in Times of COVID-19: Recommendations to Safely Return to a Sub-Saharan Africa Low-Resource Setting

2021 ◽  
Author(s):  
Víctor Lopez-Lopez ◽  
Ana Morales ◽  
Elisa García-Vazquez ◽  
Miguel González ◽  
Quiteria Hernandez ◽  
...  
Keyword(s):  
Sub Saharan Africa ◽  
Low Resource ◽  
Resource Setting ◽  
Low Resource Setting ◽  
Sub Saharan

scholarly journals Vulvar cancer: surgical management and survival trends in a low resource setting

2020 ◽  
Vol 32 (1) ◽  
Author(s):  
Navin Kumar ◽  
Mukur Dipi Ray ◽  
D. N. Sharma ◽  
Rambha Pandey ◽  
Kanak Lata ◽  
...  
Keyword(s):  
Surgical Management ◽  
Vulvar Cancer ◽  
Low Resource ◽  
Resource Setting ◽  
Low Resource Setting

Multi-level Chunk-based Constituent-to-Dependency Treebank Transformation for Tibetan Dependency Parsing

2021 ◽  
Vol 20 (2) ◽  
pp. 1-12
Author(s):  
Shumin Shi ◽  
Dan Luo ◽  
Xing Wu ◽  
Congjun Long ◽  
Heyan Huang
Keyword(s):  
Language Processing ◽  
Manual Annotation ◽  
Syntactic Parsing ◽  
Dependency Parsing ◽  
Low Resource ◽  
Resource Setting ◽  
Dependency Tree ◽  
Low Resource Setting ◽  
Novel Method ◽  
Multi Level

Dependency parsing is an important task for Natural Language Processing (NLP). However, a mature parser requires a large treebank for training, which is still extremely costly to create. Tibetan is a kind of extremely low-resource language for NLP, there is no available Tibetan dependency treebank, which is currently obtained by manual annotation. Furthermore, there are few related kinds of research on the construction of treebank. We propose a novel method of multi-level chunk-based syntactic parsing to complete constituent-to-dependency treebank conversion for Tibetan under scarce conditions. Our method mines more dependencies of Tibetan sentences, builds a high-quality Tibetan dependency tree corpus, and makes fuller use of the inherent laws of the language itself. We train the dependency parsing models on the dependency treebank obtained by the preliminary transformation. The model achieves 86.5% accuracy, 96% LAS, and 97.85% UAS, which exceeds the optimal results of existing conversion methods. The experimental results show that our method has the potential to use a low-resource setting, which means we not only solve the problem of scarce Tibetan dependency treebank but also avoid needless manual annotation. The method embodies the regularity of strong knowledge-guided linguistic analysis methods, which is of great significance to promote the research of Tibetan information processing.

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