Evidence map›Paper›PMID 42680985›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Validation of a modular low-fidelity training model for biportal endoscopic spine surgery.

Camille Niederhauser, Guillaume Haering, Blaise Pellegrini, Elias Ben Rayana, Sara Paravina, Nasim Lotfinejad, Mahmoud Messerer, Juan Barges-Coll, Dominique Rothenfluh

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Camille NiederhauserUniversity Hospital of Lausanne, Lausanne, Switzerland. camille.niederhauser@chuv.ch.
Guillaume Haering
Blaise PellegriniUniversity Hospital of Lausanne, Lausanne, Switzerland.
Elias Ben RayanaUniversity Hospital of Lausanne, Lausanne, Switzerland.
Sara ParavinaUniversity Hospital of Lausanne, Lausanne, Switzerland.
Nasim LotfinejadUniversity Hospital of Lausanne, Lausanne, Switzerland.
Mahmoud MessererUniversity Hospital of Lausanne, Lausanne, Switzerland.
Juan Barges-CollUniversity Hospital of Lausanne, Lausanne, Switzerland.
Dominique RothenfluhUniversity Hospital of Lausanne, Lausanne, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveSpinal endoscopy is associated with a steep learning curve, in part owing to its limited depth perception, which increases the need for training models. This study aimed to investigate construct and predictive validity of the Endobox as a low-fidelity training model for biportal endoscopy.

methodsTo assess construct validity, we compared the performances of expertsand novices across four Endobox exercises. Performances were evaluated based on completion time and number of attempts, using a 0º endoscope and rongeur. To assess predictive validity of the Endobox, 16 students (n = 8 per group) were randomly assigned into EB+ (twelve training sessions) or EB- (no training) before performing six simulated discectomies on a non-anatomical simulation model. Completion time, technical skill (modified version of the ASSET score), and cognitive demand (NASA-TLX score) were compared. Statistical analysis was carried out using parametric and non-parametric tests using Stata 18 for Mac.

resultsConstruct validity: experts were significantly faster in 3/4 exercises (2. p = 0.02, 3. p = 0.01, 4. p = 0.03), except in exercise 1 (p = 0.43). Experts needed fewer attempts for exercises 3 (p = 0.04) and 4 (p = 0.02), rated most difficult (9/10, 8/10 respectively). Predictive validity: the EB+ group was faster (p < 0.001) and improved across all observations, achieving higher scores for safety (p = 0.007), camera/bimanual dexterity, instrument handling, depth perception, and procedure quality (all p < 0.001). No difference in cognitive load was observed (p = 0.621).

conclusionThe present study provides preliminary evidence that the Endobox model may develop skills that are relevant to, and potentially transferable to, a simulated procedure representing endoscopic spine surgery.

Indexed as

Biportal endoscopyLearning curveLow-fidelity modelSimulation trainingSpinal endoscopyValidity assessment

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.