Challenges of biological realism and validation in simulation-based medical education

References 

1. Issenberg BS, McGaghie WC, Petrusa ER, Gordon DL, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach. 2005;10–28
2. Vozenilek J, Huff JS, Reznek M, Gordon JA. See one, do one, teach one: advanced technology in medical education. Acad Emerg Med. 2004;1149–1154
3. Ziv A, Wolpe PR, Small SD, Glick S. Simulation-based medical education: an ethical imperative. Acad Med. 2003;783–788
4. Friedman CP. The marvelous medical education machine or how medical education can be unstuck in time. Med Teach. 2000;496–502
5. Meller G. A typology of simulators for medical education. J Digital Imag. 1997;194–196
6. Ahmed M, Meech JF, Timoney A. Virtual reality in medicine. Br J Urol. 1997;46–52
7. Fletcher KE, Davis SQ, Underwood W, Mangrulkar RS, McMahon LF, Saint S. Systematic review: effects of resident work hours on patient safety. Ann Intern Med. 2004;851–857
8. Monopolies and Mergers Commission UK. XXXPrivate medical services: a report on agreements and practices relating to charges for the supply of private medical services by NHS Consultants; 2002. http://www.competition-commission.org.uk/rep_pub/reports/1994/348privmedical.htm. Accessed 20 October, 2005.
9. Lehmann ED, Deutsch T, Roudsari AV, Carson ER, Benn JJ, Sonksen PH. An integrated approach for the computer-assisted treatment of diabetic patients on insulin. Med Inform (London). 1992;105–123
10. Grenvik A, Schaefer JJ, DeVita MA, Rogers P. New aspects on critical care medicine training. Curr Opin Crit Care. 2004;233–237
11. Simon R, Freidlin B, Rubinstein L, Arbuck SG, Collins J, Christian MC. Accelerated titration designs for phase I clinical trials in oncology. J Nat Cancer Inst Cancer Spectrum. 1997;1138–1147
12. Wyatt JC, Altman DG, Heathfield HA, Pantin CFA. Development of design-a-trial, knowledge-based critiquing system for authors of clinical trial protocols. Comput Meth Programs Biomed. 1994;283–291
13. Ette EI, Williams PJ, Kim YH, Lane JR, Liu MJ, Capparelli EV. Model appropriateness and population pharmacokinetic modeling. J Clin Pharmacol. 2003;610–623
14. Brownson RC. Teaching evidence-based public health to public health practitioners. Ann Epidemiol. 2005;540–544
15. Nagarajan A, Hmelo CE, Day RS. In:  Hoadley C editors. Collaborative scientific inquiry in a simulated laboratory. Mahwah, NJ: Erlbaum; 1999;p. 416–424
16. Tabak I, Smith BK, Sandoval WA, Reiser BJ. Combining general and domain-specific strategic support for biological inquiry. In:  Frasson C,  Gauthier G,  Liu MJ editor. Intelligent Tutoring Systems, Third International Conference, Lecture Notes in Computer Science, vol. 1086. Springer; 1996;p. 288–296
17. Michael JA, Rovick AA. Problem-solving in the pre-clinical curriculum: the uses of computer simulations. Med Teach. 1986;19–25
18. Ambrose RF, Kendall LG, Alarcon GS, Brown S, Lipstate JM, Wirtschafter DD, et al. Rheumatology algorithms for primary care physicians. Arthritis Care Res. 1990;71–77
19. Croskerry P, Wears RL, Binder LS. Setting the educational agenda and curriculum for error prevention in emergency medicine. Acad Emerg Med. 2000;1194–1200
20. Rovick AA, Michael JA. The predictions table: a tool for assessing students’ knowledge. Am J Physiol. 1992;S33–S36
21. Day RS, Shirey W, Ramakrishnan S, Huang Q. Tumor biology modeling workbench for prospectively evaluating cancer treatments. In:  Borne P,  Ksouri M,  El Kamel A editor. Proceedings of the IEEE /CESA ’98: Computational Engineering with Systems Applications. Tunisia: Hammamet; 1998;(Accessed October 24, 2005: http://www.eecs.umich.edu/∼sailesh/tunisiafinal98.pdf)
22. Friedman CP, Gatti GG, Murphy GC, Franz TM, Fine PL, Heckerling PS, et al. Exploring the boundaries of plausibility: empirical study of a key problem in the design of computer-based clinical simulations. Proc Am Med Inform Assoc Symp. 2002;275–279
23. Shackney SE, Smith CA, Pollice A, Brown K, Day R, Julian T, et al. Intracellular patterns of Her-2/neu, ras, and ploidy abnormalities in primary human breast cancers predict postoperative clinical disease-free survival. Clin Cancer Res. 2004;3042–3052
24. Day RS. A branching-process model for heterogeneous cell populations. Math BioSci. 1986;73–90
25. Day RS. Treatment sequencing, asymmetry, and uncertainty: protocol strategies for combination chemotherapy. Cancer Res. 1986;3876–3885
26. Goldie JH, Coldman AJ. A mathematic model for relating the drug sensitivity of tumors to their sponteneous mutation rate. Cancer Treat Rep. 1979;1727–1733
27. Norton L, Day RS. Potential innovations in scheduling cancer chemotherapy. In:  DeVita VT,  Hellman S,  Rosenberg SA editor. Important advances in oncology. Philadelphia: J.B. Lippincott Company; 1991;p. 57–71
28. Hmelo C, Ramakrishnan S, Day RS, Shirey WE, Brufsky A, Johnson C, et al. The Oncology Thinking Cap: scaffolded use of a simulation to learn clinical trial design. Teach Learn Med. 2001;183–191
29. Day RS. Exploring large tumor model spaces: drawing sturdy conclusions. In:  Thompson J,  Brown B editor. Cancer modeling. New York: Marcel Dekker, Inc.; 1987;p. 365–385
30. Day RS, Manning BK, Garrott D, Brufsky AM, Baar J, Egorin MJ, et al. Evaluation of the OncoTCap (oncology thinking cap) simulation in teaching medical students about clinical trials: some successes and some surprises. J Cancer Educ. 2004;149–155
31. Simon R. Optimal two-stage designs for phase II clinical trials. Contr Clin Trials. 1989;1–10
32. Mendel J. Introduction. In: Uncertain rule-based fuzzy logic systems. Upper Saddle River, NJ: Prentice Hall, Inc.; 2001;p. 26–31
33. Ferguson RE, Taylor C, Stanley A, Butler E, Joyce A, Harnden P, et al. Resistance to the tubulin-binding agents in renal cell carcinoma: no mutations in the class I beta-tubulin gene but changes in tubulin isotype protein expression. Clin Cancer Res. 2005;3439–3445
34. Petrini MF, Thomas DR, Mahan JM. Teaching medical decision analysis: the hepatitis B vaccine. Comput Biol Med. 1987;351–355
35. Kakazu KK, Cheung LW, Lynne W. The Cancer Biomedical Informatics Grid (caBIG): pioneering an expansive network of information and tools for collaborative cancer research. Hawaii Med J. 2004;273–275
36. Buetow KH. The NCI Center for Bioinformatics (NCICB): building a foundation for in silico biomedical research. Cancer Invest. 2004;117–122
37. Papa FJ, Elieson B. Diagnostic accuracy as a function of case prototypicality. Acad Med. 1993;S58–S60
38. Papa FJ, Stone RC, Aldrich DG. Further evidence of the relationship between case typicality and diagnostic performance: implications for medical education. Acad Med. 1996;S10–S12
39. Marsaglia G, Zaman A. A new class of random number generators. Ann Appl Prob. 1991;462–480
40. Martin S, Jackson A. Knowledge Integrated Modeling (KIM), an application for the Semantic Layered Research Platform (SLRP). In: W3C Workshop on Semantic Web for Life Sciences. 2004;(Accessed October 24, 2005: http://lists.w3.org/Archives/Public/public-swls-ws/2004Sep/att-0069/Semantic_Layered_Research_Platform.pdf)
41. Jadad AR, Cook DJ, Jones A, Klassen TP, Tugwell P, Moher M, et al. Methodology and reports of systematic reviews and meta-analyses: a comparison of Cochrane reviews with articles published in paper-based journals. J Am Med Assoc. 1998;278–280
42. Meller G. A typology of simulators for medical education. J Digital Imag. 1998;194–196
43. Crowley RS, Medvedeva O. A general architecture for intelligent tutoring of diagnostic classification problem solving. Am Med Inform Assoc Annu Symp Proc. 2003;185–189
44. Behling LL. Replacing the patient: the fiction of prosthetics in medical practice. J Med Humanit. 2005;53–66
45. Tedesco, et al. Docetaxel combined with trastuzumab is an active regimen in HER-2,3+ overexpressing and FISH-+ metastatic breast cancer. J Clin Oncol 2004.