Evidence map›Paper›PMID 40506030›Full record

ArticleAsian spine journal2026

Lumbar spinal stenosis: current concept of management.

Ji-Won Kwon, Kyung-Soo Suk, Seong-Hwan Moon, Si-Young Park, Namhoo Kim, Sub-Ri Park, Jae-Won Shin, Hak-Sun Kim, Byung Ho Lee

Abstract read
In one paragraph

Article in Asian spine journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Trial
  2. Endoscopic decompression alone for lumbar spinal stenosis with degenerative spondylolisthesis: a systematic review and meta-analysis of paired change scores.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
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ji-Won KwonDepartment of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea.
Kyung-Soo SukDepartment of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea.
Seong-Hwan MoonDepartment of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea.
Si-Young ParkDepartment of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea.
Namhoo KimDepartment of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea.
Sub-Ri ParkDepartment of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea.
Jae-Won ShinDepartment of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea.
Hak-Sun KimDepartment of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea.
Byung Ho LeeDepartment of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lumbar spinal stenosis (LSS) is a common degenerative spinal condition where spinal canal narrowing causes symptoms such as neurogenic claudication, radiculopathy, and lower back pain. While non-operative and surgical approaches yield similar long-term outcomes, surgical intervention-particularly decompression-can provide earlier symptom relief, functional recovery, and fall prevention in selected patients with refractory symptoms. Recent advancements in surgical technologies and image guidance have brought about a paradigm shift in LSS management. Biportal endoscopic spine surgery (BESS) has gained global traction as a minimally invasive alternative to traditional decompression methods, offering superior visualization, less soft tissue damage, shorter hospital stays, and faster recovery. High-quality studies, including randomized controlled trials, have shown promising outcomes for this technique. Furthermore, the integration of navigation systems, robot-assisted instrumentation, and artificial intelligence (AI)-driven diagnostics and surgical planning tools is transforming spinal surgery by enhancing precision in preoperative evaluation and intraoperative execution. These innovations enable accurate targeting, reduce complications, and improve reproducibility across diverse surgical settings. This review provides an updated overview of LSS, covering its pathophysiology, clinical assessment, diagnosis, and treatment. Special emphasis is placed on the growing role of BESS and the transformative impact of digital technologies such as navigation, robotics, and AI in the evolving landscape of spinal stenosis care.

Indexed as

Artificial intelligenceBiportal endoscopic spine surgeryLumbar spinal stenosisNavigationRobotics

Identifiers

PMID40506030
PMCPMC12960425

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.