Evidence map›Paper›PMID 35245836›Full record

ArticleJournal of biomechanics2022

The relationship of whole human vertebral body creep to bone density and texture via clinically available imaging modalities.

Daniel Oravec, Woong Kim, Michael J Flynn, Yener N Yeni

Open access · greenAbstract read
In one paragraph

Article in Journal of biomechanics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 55% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

4 authors at 1 institution in 1 country.

Daniel OravecBone and Joint Center, Henry Ford Hospital, Detroit, MI, United States.
Woong KimBone and Joint Center, Henry Ford Hospital, Detroit, MI, United States.
Michael J FlynnDepartment of Radiology, Henry Ford Hospital, Detroit, MI, United States.
Yener N YeniBone and Joint Center, Henry Ford Hospital, Detroit, MI, United States. Electronic address: yeni@bjc.hfh.edu.
Henry Ford Hospital · US

Funding

Digital Tomosynthesis-Based Microstructural Measures to Predict Vertebral FragiliR21AR059329 · NIAMS · HENRY FORD HEALTH SYSTEM · PI YENI, YENER N · 2011 to 2012
$363k
NIAMS NIH HHS R21 AR059329
6 · The paper itself

Abstract

Creep deformation of human vertebrae accumulates under physiological levels of load and is understood to contribute to the progression toward clinically observable vertebral fracture. However, little information is available in terms of clinically measurable predictors of creep behavior in human vertebrae. In this study, creep tests were performed on 22 human cadaveric T12 vertebrae (13 male, 9 female; age 41-90). Areal and volumetric bone density parameters were measured from the same specimens using dual x-ray absorptiometry and high resolution computed tomography. Image textural analyses (which probe the organization of image intensities within the cancellous bone in low resolution clinical imaging) were performed using digital tomosynthesis (DTS) images. Multiple regression models were constructed to examine the relationship between creep properties and bone density and DTS image textural parameters. For the standard clinical imaging configuration, models including DTS derived image textural parameters alone were generally more explanatory (adjusted R

Indexed as

Bone DensitySpinal FracturesAdultAgedAged, 80 and overCancellous BoneFemaleHumansLumbar VertebraeMaleMiddle AgedThoracic VertebraeVertebral BodyCreepDigital tomosynthesisMaterial propertiesVertebral body

Identifiers

PMID35245836
PMCPMC9064953
OpenAlexW4213455642

What OpenQuestion holds

Textmetadata
LicenceTDM
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.