Evidence map›Paper›PMID 33553381›Full record

ReviewAnnals of translational medicine2021

Narrative review of intraoperative imaging guidance for decompression-only surgery.

Sohrab Virk, Sheeraz Qureshi

Open access · diamondAbstract readReview
In one paragraph

Review in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 40% of its field
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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Intraoperative Navigation in Cervical Spine Surgery.Journal of clinical medicine · 2026
    Review
  2. Review
  3. Article
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

2 authors at 2 institutions in 1 country.

Sohrab VirkDepartment of Orthopedic Surgery, Northwell Health, Great Neck, NY, USA.
Sheeraz QureshiDepartment of Orthopedic Surgery, Hospital for Special Surgery, New York, NY, USA.
Hospital for Special Surgery · USNorthwell Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Decompression of the spine is defined as removal of bony and soft tissue structures in order to provide space for the spinal cord and/or nerve roots. This definition, however, underscores the dangers and complexity of safely providing anatomical space for these neurologic structures. Complications such as neurologic injury, vascular injury, and durotomy can make these procedures hazardous for the patient and surgeon. Furthermore, inability to fully decompress the neural elements will result in continued symptoms for patients. Intraoperative image guidance can provide important anatomical landmarks to perform these decompressive surgeries safely and efficiently. In particular, performing decompression surgery utilizing minimally invasive techniques with image guidance can allow for the least amount of muscle/soft tissue trauma possible. Within our article we outline research on the forefront of use of intra-operative imaging guidance for spine surgery and implications for decompression surgery. We also outline a case from the senior author to illustrate an example of image-guided spine decompression for cervical radiculopathy. Future technology, such as augmented reality and robotics, is also discussed in the context of image guided decompression. The authors hope this article shows surgeons that use of image guidance in specific clinical situations can allow for better/safer spinal decompression procedures.

Indexed as

decompression-only surgeryIntraoperative imaging guidancelocalization

Identifiers

PMID33553381
PMCPMC7859763
OpenAlexW3125218867

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.