ArticleJournal of neurosurgery. Case lessons2024
Placement of 2 cages using navigation-guided biportal endoscopic transforaminal lumbar interbody fusion: illustrative case.
Article in Journal of neurosurgery. Case lessons, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Advancements in unilateral biportal endoscopic lumbar interbody fusion: a narrative review.European journal of medical research · 2026Review
- Use of Double Cages for Biportal Endoscopic Transforaminal Lumbar Interbody Fusion: A Comparison of 3-Dimensional-Printed Titanium and Polyetheretherketone Cages.International journal of spine surgery · 2025Article
- O-arm navigation-guided unilateral biportal endoscopic lumbar interbody fusion using a lateral lumbar interbody fusion cage.Asian spine journal · 2025Article
- O-arm navigation-based transforaminal unilateral biportal endoscopic discectomy for upper lumbar disc herniation: an innovative preliminary study.Asian spine journal · 2025Article
- The Utilization of Navigation and Emerging Technologies With Endoscopic Spine Surgery: A Narrative Review.Neurospine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn recent developments, full endoscopic and unilateral biportal endoscopic (UBE) spine surgery have emerged to aid the transforaminal lumbar interbody fusion (TLIF) procedure. Yet, both approaches present a challenge due to limited space for cage insertion, potentially leading to complications such as cage subsidence or nonfusion in long-term assessments. Utilizing double cages may mitigate these concerns. This paper presents a unique case in which a patient successfully underwent computed tomography (CT) navigation-guided UBE-TLIF with 2 converging cages, highlighting the potential benefits and feasibility of this innovative approach. OBSERVATIONS: A 59-year-old female diagnosed with degenerative spondylolisthesis at the L4-5 level underwent a UBE-TLIF. The operation is detailed step by step and supported by illustrative figures and surgical videos. Postsurgery results revealed a significant improvement in the patient's condition, with the visual analog scale score decreasing from 7 to 3 on the first day, leading to a satisfaction rate of 90% at the last follow-up. LESSONS: Utilizing endoscopic visualization complemented by contrast medium has substantially elevated the quality of disc preparation. From their observations, the authors affirm that the integration of intraoperative CT navigation systems significantly augments safety and pinpoint accuracy in UBE-TLIF procedures. The strategy of employing 2 converging cages through a unilateral technique stands as a practical solution, potentially optimizing the fusion outcomes of UBE-TLIF surgery. https://thejns.org/doi/10.3171/CASE23512.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.