Evidence map›Paper›PMID 40896278›Full record

ArticleAging medicine (Milton (N.S.W))2025

Spino-Pelvic Alignment and Surgical Outcomes in Chinese Elderly With Lumbar Stenosis Syndrome: A Prospective Study.

Chong Zhao, Linyu Jin, Yan Liang, Haiying Liu, Shuai Xu

Abstract read
In one paragraph

Article in Aging medicine (Milton (N.S.W)), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Chong ZhaoDepartment of Spinal Surgery Peking University People's Hospital, Peking University Beijing People's Republic of China.ORCID https://orcid.org/0000-0003-0257-9928
Linyu JinDepartment of Orthopedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopedics Ruijin Hospital, Shanghai Jiaotong University School of Medicine Shanghai China.
Yan LiangDepartment of Spinal Surgery Peking University People's Hospital, Peking University Beijing People's Republic of China.
Haiying LiuDepartment of Spinal Surgery Peking University People's Hospital, Peking University Beijing People's Republic of China.
Shuai XuDepartment of Spinal Surgery Peking University People's Hospital, Peking University Beijing People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to systematically identify and quantify the acceptable spino-pelvic parameters for Chinese elderly patients with lumbar stenosis syndrome (LSS) following surgical treatment. Method: A prospective study of 165 LSS cases from July 2018 to June 2020 evaluated spino-pelvic alignment using PI, LL, PT, and SVA, categorized by SRS-Schwab classification. Quality of life was assessed via the Oswestry disability index (ODI), and parameter thresholds were determined by AICc and ROC analysis. Results: No significant change in radiological parameters was observed postsurgery. However, clinical outcomes improved with lower grades of PI-LL and PT. The mean threshold for PI-LL and PT at baseline was 18.8° and 26.1°, respectively. At the endpoint, PI-LL was 17.7°, and PT was 26.1°, indicating optimal ranges for surgery. Conclusion: Spino-pelvic alignment significantly correlates with quality of life in elderly LSS patients. The study suggests that PI-LL and PT should be maintained within 17.7° and 26.1°, respectively, for optimal surgical outcomes.

Indexed as

biomechanics of spino‐pelvic interactionlumbar stenosis syndrome (LSS)spino‐pelvic sagittal alignmentSRS‐Schwab classification

Identifiers

PMID40896278
PMCPMC12399335

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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.