Board #322 - Technology Innovation Development and Preliminary Validation of a Novel Ventriculostomy Simulator (Submission #8635)
Bibliographic record
Abstract
Introduction A team of biomedical engineers, neurosurgeons, and a medical educator, created and evaluated preliminary validity evidence of a high-fidelity simulator used to train ventriculostomy skills. Description A scale reproduction of an adult skull and relevant anatomy was created. Twelve neurosurgery residents, one fellow, and five attendings (n=17) from three academic medical centers performed the simulated creation of a ventricolostomy. Participants rated the simulator using a 37-item survey across five domains. Quality of the simulator was scored from “Not at all realistic” (1) to “Highly realistic” (4). Self-reported ability to perform relevant tasks was rated from “Too difficult to perform (0) to “Too easy to perform” (4). Validity evidence relevant to test content was evaluated using a Rasch model, while evidence relevant to internal structure (inter-item reliability, and inter-rater agreement) was evaluated using traditional methods. Conclusion Analyses indicated attendings had statistically higher ratings (M=3.4/4.0) than fellows (M=3.3), and residents (M=3.0), p=.02. Domain means were 3.9 (Value), 3.5 (Physical attributes), 3.4 (Realism of experience), 3.3 (Relevance), and 2.9 (Ability). Inter-item consistency across domains were estimated to be moderate-high (.65, .92), and inter-rater agreement regarding simulator characteristics was high [ICC(2,k)= .90]. Rating differences were found across institutions, p=.001, and are reviewed. The observed global rating (2.5) indicated the simulator can be considered for teaching ventriculostomies, but could be improved slightly. Ratings indicated the simulator was valuable as a learning tool, but could be improved with minor modifications. The most commonly-suggested improvement was the addition of ears as an added anatomical landmark. References 1. Haji FA, Dubrowski A, Drake J, de Ribaupierre S. Needs assessment for simulation training in neuroendoscopy: a Canadian national survey. J Neurosurg. 2013 Feb;118(2):250-7. doi: 10.3171/2012.10.JNS12767. Epub 2012 Dec 7 2. Schirmer CM, Elder JB, Roitberg B, Lobel DA. Virtual reality-based simulation training for ventriculostomy: an evidence-based approach. Neurosurgery. 2013 Oct;73 Suppl 1:66-73. doi: 10.1227/NEU.0000000000000074. 3. Choudhury N, Gélinas-Phaneuf N, Delorme S, Del Maestro R. Fundamentals of neurosurgery: virtual reality tasks for training and evaluation of technical skills. World Neurosurg. 2013 Nov;80(5):e9-19. doi: 10.1016/j.wneu.2012.08.022. Epub 2012 Nov 23. 4. Korndorffer JR Jr, Kasten SJ, Downing SM. A call for the utilization of consensus standards in the surgical education literature. Am J Surg. 2010 Jan;199(1):99-104. doi: 10.1016/j.amjsurg.2009.08.018 5. Cook DA, Brydges R, Zendejas B, Hamstra SJ, Hatala R. Technology-enhanced simulation to assess health professionals: A systematic review of validity evidence, research methods, and reporting quality. Acad Med. 2013 Jun;88(6):872-83. doi: 10.1097/ACM.0b013e31828ffdcf. 6. Cook DA, Zendejas B, Hamstra SJ, Hatala R, Brydges R. What counts as validity evidence? Examples and prevalence in a systematic review of simulation-based assessment. Adv Health Sci Educ Theory Pract. 2014 May;19(2):233-50. doi: 10.1007/s10459-013-9458-4. Epub 2013 May 2. 7. Standards for Educational and Psychological Testing, 1999, American Educational Research Association, American Psychological Association and National Council on Measurement in Education: American Educational Research. 8. Rasch G. (1960/1980). Probabilistic models for some intelligence and attainment tests. (Copenhagen, Danish Institute for Educational Research), expanded edition (1980) with foreword and afterword by BD Wright. Chicago, IL: The University of Chicago Press. 9. Hamilton JM, et al., Toward effective pediatric minimally invasive surgical simulation. J Pediatr Surg, 2011. 46(1): p. 138-44. Disclosures None
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.028 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".