Evidence map›Paper›PMID 37364939›Full record

ArticleInternational journal of spine surgery2023

Real-World Implementation of Artificial Intelligence/Machine Learning for Managing Surgical Spine Patients at 2 Academic Health Care Systems.

Ghaith Habboub, Sigurd Berven, Christopher Ames, Thomas Peterson, Thomas Mroz

Abstract read
In one paragraph

Article in International journal of spine surgery, 2023. 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. 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

5 authors.

Ghaith HabboubCleveland Clinic Center for Spine Health, Cleveland, OH, USA habboug@ccf.org.
Sigurd BervenDepartment of Orthopedic Surgery, UCSF Medical Center, San Francisco, CA, USA.
Christopher AmesDepartment of Neurological Surgery, University of California, San Francisco, CA, USA.
Thomas PetersonDepartment of Orthopedic Surgery, UCSF Medical Center, San Francisco, CA, USA.
Thomas MrozCleveland Clinic Center for Spine Health, Cleveland, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Decision-making in spine surgery is complex due to patients' heterogeneity and complexity of spinal pathologies and the various surgical options applied to a given pathology. Artificial intelligence/machine learning algorithms provide an opportunity to improve patient selection, surgical planning, and outcomes. The purpose of this article is to present the experience and applications of in spine surgery at 2 large academic health care systems.

Indexed as

artificial intelligencepredictive modelingspine surgery

Identifiers

PMID37364939
PMCPMC10318912

What OpenQuestion holds

Textmetadata
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.