Neurosurgical focus
October 1, 2026 · via Europe PMC
Development and prospective validation of a porcine simulation model for training in unilateral biportal endoscopic spine surgery.
Bakhaidar M, Alfawaz T, Perez Rodriguez Garcia G, Nessek H, Tsai EC, Coyle M, Phan P.
AI summary — for doctors, generated from the abstract only
A porcine model for biportal endoscopic spine training showed validity and short-term improvement after a single session.
What was studied
Prospective educational study at the University of Ottawa with 36 participants of varying experience using a porcine UBE lumbar decompression curriculum with pre- and posttraining testing.
Key findings
- Face and content validity scores were high (median 6-7 on a 7-point scale).
- Advanced participants outperformed novices at baseline on all tasks, supporting construct validity.
- Performance improved after a single structured session.
Clinical relevance
UBE has a steep learning curve and simulation can provide structured training.
Who should care
Limitations
Predictive validity and skill retention were not shown, and only 27 completed both assessments.
Consider porcine simulation to start UBE training, then confirm clinical transfer.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Objective
Unilateral biportal endoscopic spine surgery (UBE) offers several advantages over traditional techniques but is associated with a steep learning curve and limited access to structured training. Simulation-based education may facilitate skill acquisition, however, validated training platforms for UBE remain limited. This study aimed to develop and validate a porcine simulation model for UBE lumbar decompression and evaluate its educational impact using established simulation validity frameworks.
Methods
A prospective educational study was conducted at the University of Ottawa between October 2025 and April 2026. Thirty-six participants with varying levels of surgical experience completed a structured curriculum consisting of baseline assessment, instructional training, and posttraining evaluation. Performance was assessed using a 7-point Likert-based global rating scale and task completion time across 3 standardized tasks. Face, content, and construct validity were evaluated through participant questionnaires and performance comparisons. Pre- and posttraining performance was analyzed using paired statistical tests. Univariate and multivariate linear regression analyses were performed to identify predictors of baseline performance.
Results
Twenty-seven participants completed both pre- and posttraining assessments; 35 participants contributed baseline data for construct validity analysis. The simulator demonstrated high face and content validity, with median Likert scores of 6-7. Construct validity was supported by significantly higher baseline performance among advanced participants compared with novices across all tasks (task 1 combined total score, p = 0.003; task 2, p = 0.007; task 3, p < 0.001). Following training, significant improvements were observed across all tasks and performance domains (all p < 0.001). Task completion times also decreased significantly across all tasks (all p < 0.001). Regression analysis identified surgical experience as a key predictor of baseline performance.
Conclusions
The porcine UBE simulation model represents a cost-effective training platform with established face, content, and construct validity and produces measurable short-term improvements in simulator performance after a single structured session. This model provides a practical approach to support structured training in UBE spine surgery. Predictive validity, long-term skill retention, and whether simulation-acquired skills shorten the operative learning curve remain hypotheses for future study.
DOI
10.3171/2026.7.FOCUS26328
PMID
42822043
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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