Evidence map›Paper›PMID 39336947›Full record

ReviewJournal of clinical medicine2024

Challenges in Contemporary Spine Surgery: A Comprehensive Review of Surgical, Technological, and Patient-Specific Issues.

Emmanuel O Mensah, Joshua I Chalif, Jessica G Baker, Eric Chalif, Jason Biundo, Michael W Groff

Abstract readReview
In one paragraph

Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 2 pooled it
–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

29 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  19. Artificial Intelligence in Planning for Spine Surgery.Current reviews in musculoskeletal medicine · 2025
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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

6 authors.

Emmanuel O MensahDepartment of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.ORCID 0000-0002-4644-8674
Joshua I ChalifDepartment of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
Jessica G BakerDepartment of Behavioral Neuroscience, Northeastern University, Boston, MA 02115, USA.
Eric ChalifDepartment of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
Jason BiundoF.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA.
Michael W GroffDepartment of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.ORCID 0000-0003-1873-9578

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spine surgery has significantly progressed due to innovations in surgical techniques, technology, and a deeper understanding of spinal pathology. However, numerous challenges persist, complicating successful outcomes. Anatomical intricacies at transitional junctions demand precise surgical expertise to avoid complications. Technical challenges, such as underestimation of the density of fixed vertebrae, individual vertebral characteristics, and the angle of pedicle inclination, pose additional risks during surgery. Patient anatomical variability and prior surgeries add layers of difficulty, often necessitating thorough pre- and intraoperative planning. Technological challenges involve the integration of artificial intelligence (AI) and advanced visualization systems. AI offers predictive capabilities but is limited by the need for large, high-quality datasets and the "black box" nature of machine learning models, which complicates clinical decision making. Visualization technologies like augmented reality and robotic surgery enhance precision but come with operational and cost-related hurdles. Patient-specific challenges include managing postoperative complications such as adjacent segment disease, hardware failure, and neurological deficits. Effective patient outcome measurement is critical, yet existing metrics often fail to capture the full scope of patient experiences. Proper patient selection for procedures is essential to minimize risks and improve outcomes, but criteria can be inconsistent and complex. There is the need for continued technological innovation, improved patient-specific outcome measures, and enhanced surgical education through simulation-based training. Integrating AI in preoperative planning and developing comprehensive databases for spinal pathologies can aid in creating more accurate, generalizable models. A holistic approach that combines technological advancements with personalized patient care and ongoing education is essential for addressing these challenges and improving spine surgery outcomes.

Indexed as

artificial intelligence in spine surgeryminimally invasive surgeryspinal anatomy complexityspine surgery challengestechnological advancements

Identifiers

PMID39336947
PMCPMC11432351

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.