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  1. AU="Jingping, Lin"
  2. AU="Yoon, Sangwook"
  3. AU="Sedor, John R."
  4. AU="Legrand, Julien"
  5. AU="Mintz, Kevin Todd"
  6. AU="Kösters, Markus"
  7. AU="Castano-Duque, Lina"
  8. AU="Lowry, Gregory V"
  9. AU="Gao, Xiaojuan"
  10. AU="Daniłowicz-Szymanowicz, Ludmiła"
  11. AU="Weber, Jesse N"
  12. AU="Fages-Masmiquel, Ester"
  13. AU="Macias Gil, Raul"
  14. AU="Planchat, Arnaud"
  15. AU="McElrath, Erin E"
  16. AU="Koji Ueda"
  17. AU="Pillas, Diana J"
  18. AU="Thomson, Jason J"
  19. AU="Mitra, Kalyan"
  20. AU="Sanjay Desai"
  21. AU=Cox David J AU=Cox David J
  22. AU="Grebenok, Robert J."
  23. AU="Blackburne, Brittney"
  24. AU="Bortoleti, Bruna Taciane da Silva"
  25. AU="Ehrbar, Martin"
  26. AU="Lepre, Davide"
  27. AU="Olszewska, Zuzanna"
  28. AU="Vojta, Leslie"
  29. AU=Wickstrom Eric AU=Wickstrom Eric
  30. AU="Gangavarapu, Sridevi"
  31. AU="Hussein, Hazem Abdelwaheb"
  32. AU=Cai Yixin AU=Cai Yixin
  33. AU="Hüls, Anke"
  34. AU="Poondru, Srinivasu"
  35. AU="Coca, Daniel"
  36. AU="Lebeau, Paul"
  37. AU="Dehghani, Sedigheh"
  38. AU="Ishibashi, Kenji"
  39. AU="Xu, Yanhua"
  40. AU="Matera, Katarzyna"
  41. AU="Ait-Ouarab, Slimane"
  42. AU="Nicola, Coppede"
  43. AU="Dewitt, John M"
  44. AU="Sorin M. Dudea"
  45. AU="Tanusha D. Ramdin"
  46. AU="Hao, Zehui"
  47. AU="Chauhan, Aman"

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  1. Artikel ; Online: A low cost surrogate eye model for corneal foreign body removal

    Jingping Lin / Mui Teng Chua

    BMC Ophthalmology, Vol 20, Iss 1, Pp 1-

    2020  Band 7

    Abstract: Abstract Background Patients commonly present to the Emergency Department with a corneal foreign body (FB). There is currently a lack of adequate training for junior doctors in the management of this condition. Our self-made surrogate eye model aims to ... ...

    Abstract Abstract Background Patients commonly present to the Emergency Department with a corneal foreign body (FB). There is currently a lack of adequate training for junior doctors in the management of this condition. Our self-made surrogate eye model aims to address this void in our junior doctors’ knowledge. Methods Participants were guided through a hands-on session with a slit-lamp using our eye model, which is made of a hemispherical agar embedded with pencil lead fragments simulating as FBs. Using a 7-point Likert scale, all participants completed a questionnaire both before and after training, for: (1) knowledge in corneal FB removal, (2) confidence in corneal FB removal, and (3) effectiveness of the model. Results Out of 73 participants, 82.2% (60/73) had no prior experience in corneal FBs removal. After the training session, their knowledge improved from a median score of 2 (interquartile range [IQR] 1 to 3) to 5 (IQR 5 to 6), with improvement in confidence levels from 2 (IQR 1 to 2) to 5 (IQR 4 to 6). The effectiveness of our eye model scored a median of 6 (IQR 5 to 7). Conclusions Our surrogate eye model is low-cost, quick and easy to reproduce. After use, our learners expressed greater confidence in managing the removal of corneal FBs and use of slit lamp. With a recent focus in patient safety and quality, teaching this procedure via simulation is a safe way of bridging the gap between traditional didactic teaching and the clinical environment.
    Schlagwörter Simulation ; Medical education ; Emergency medicine ; Ophthalmology ; Curriculum ; Cornea ; RE1-994
    Sprache Englisch
    Erscheinungsdatum 2020-02-01T00:00:00Z
    Verlag BMC
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Analysis of the factors influencing moderate to poor performance status in patients with cancer after chemotherapy: a cross-sectional study comparing three models.

    Xi, Ke / Jingping, Lin / Yaqing, Liu / Xinyuan, Yu / Hui, Lin / Mei, Yang / Qingyue, Chen / Dun, Liu

    Scientific reports

    2024  Band 14, Heft 1, Seite(n) 3336

    Abstract: There are no models for assessing the factors that determine moderate to poor performance status in patients with cancer after chemotherapy. This study investigated the influencing factors and identified the best model for predicting moderate-poor ... ...

    Abstract There are no models for assessing the factors that determine moderate to poor performance status in patients with cancer after chemotherapy. This study investigated the influencing factors and identified the best model for predicting moderate-poor performance status. A convenience sampling method was used. Demographic and clinical data and evaluation results for fatigue, pain, quality of life and Eastern Cooperative Oncology Group status were collected three days after the end of chemotherapy. Decision tree, random forest and logistic regression models were constructed. Ninety-four subjects in the case group had moderate to poor performance status, and 365 subjects in the control group had no or mild activity disorders. The random forest model was the most accurate model. Physical function, total protein, general quality of life within one week before chemotherapy, hemoglobin, pain symptoms and globulin were the main factors. Total protein and hemoglobin levels reflect nutritional status, and globulin levels are an index of liver function. Therefore, physical function, nutritional status, general quality of life and pain symptoms within one week before chemotherapy and liver function can be used to predict moderate-poor performance status. Nurses should pay more attention to patients with poor physical function, poor nutritional status, lower quality of life and pain symptoms after chemotherapy.
    Mesh-Begriff(e) Humans ; Quality of Life ; Cross-Sectional Studies ; Neoplasms/drug therapy ; Pain ; Globulins ; Hemoglobins
    Chemische Substanzen Globulins ; Hemoglobins
    Sprache Englisch
    Erscheinungsdatum 2024-02-09
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-024-53481-7
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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