Evidence map›Paper›PMID 41103873›Full record

ArticleCureus2025

Minimally Invasive Unilateral Lumbar Decompression in the Lateral Position Under Spinal Anesthesia: A Retrospective Study.

Maya Ghazi, Melissa Matar, Jad Al Masri, Elie Fahed, Ali Msheik, Philippe Younes

Abstract read
In one paragraph

Article in Cureus, 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

6 authors.

Maya GhaziNeurology, Lebanese American University Medical Center, Beirut, LBN.
Melissa MatarFaculty of Medical Sciences, Lebanese University, Beirut, LBN.
Jad Al MasriFaculty of Medical Sciences, Lebanese University, Beirut, LBN.
Elie FahedNeurological Surgery, Bellevue Medical Center, Beirut, LBN.
Ali MsheikNeurosurgery, Hamad Medical Corporation, Doha, QAT.
Philippe YounesFaculty of Medical Sciences, Lebanese University, Beirut, LBN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Lumbar spinal stenosis (LSS) is a debilitating condition characterized by the narrowing of the lumbar spinal canal, often affecting older adults. This study evaluates the efficacy and clinical outcomes of a novel minimally invasive decompression technique via a unilateral approach under spinal anesthesia in a lateral position. Methods A retrospective monocentric observational study was conducted from January 2023 to January 2024, including 74 patients who underwent surgery between 2018 and 2023. Data was collected through a 30-question survey, utilizing validated scales from the literature, and analyzed using the IBM SPSS Statistics for Windows, Version 25 (Released 2017; IBM Corp., Armonk, New York, United States). Results The average patient age was 69, with 47 males and 27 females. Most patients were uneducated (n = 39), and half experienced maximal pain before surgery (n = 33). One month post-surgery, 30 patients had no pain, and 24 had minimal pain. Education in non-healthcare fields was associated with greater improvement in the Oswestry Disability Index (ODI) (p = 0.037). Four patients had a dural tear. The average surgery duration was 105 minutes, ranging from 70 to 190 minutes. Conclusion This minimally invasive technique offers significant pain relief and improved disability outcomes, suggesting a quicker recovery and better quality of life for patients. Further research is needed to refine patient selection, optimize surgical techniques, and evaluate long-term outcomes.

Indexed as

lebanonlumbar spinal stenosisminimally invasivespinal anesthesiaspine surgery

Identifiers

PMID41103873
PMCPMC12527401

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Registered trials

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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.