Evidence map›Paper›PMID 30116975›Full record

ReviewCurrent osteoporosis reports2018

Vertebral Fracture Identification as Part of a Comprehensive Risk Assessment in Patients with Osteoporosis.

John T Schousboe

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current osteoporosis reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.0field-weighted citation impact, top 26% 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

7 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Current applications of deep learning in vertebral fracture diagnosis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025
    Review
  3. Article
  4. Article
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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

1 author at 1 institution in 1 country.

John T SchousboePark Nicollet Osteoporosis Center, HealthPartners Inc., Bloomington, MN, USA. scho0600@umn.edu.
HealthPartners · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewTo review current evidence regarding the vertebral fracture prevalence, the accuracy of vertebral fracture identification on current imaging technologies, and the potential impact of vertebral fracture identification on fracture risk. RECENT

findingsImportant new studies have clarified the features of prevalent vertebral fracture that most strongly predict incident fractures. Age- and sex-stratified estimates of vertebral fracture prevalence on densitometric lateral spine images in the US population are now available. The accuracy of densitometric vertebral fracture assessment, how computed tomography scans and other spinal images obtained for indications other than vertebral fracture assessment can be leveraged to detect prevalent vertebral fractures, and the potential impact of vertebral fracture assessment on patient and provider fracture risk management behavior have been clarified. Substantial progress has been made regarding screening strategies using lateral spine imaging to detect prevalent vertebral fracture in the older population. Further research regarding implementation of these strategies in clinical practice and their impact on clinical outcomes is needed.

Indexed as

Absorptiometry, PhotonHumansOsteoporosisOsteoporotic FracturesPrevalenceRisk AssessmentSpinal FracturesTomography, X-Ray ComputedUnited StatesFracture risk assessmentVertebral fractureVertebral fracture assessment

Identifiers

PMID30116975
OpenAlexW2885587067

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.