Evidence map›Paper›PMID 39224571›Full record

ArticleJBMR plus2024

Multi-site phantomless bone mineral density from clinical quantitative computed tomography in males.

Zachary A Haverfield, Amanda M Agnew, Kathryn Loftis, Jun Zhang, Lauren E Hayden, Randee L Hunter

Abstract read
In one paragraph

Article in JBMR plus, 2024. 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

6 authors.

Zachary A HaverfieldInjury Biomechanics Research Center, The Ohio State University, Columbus, Ohio 43210, United States.ORCID https://orcid.org/0000-0001-8700-4925
Amanda M AgnewInjury Biomechanics Research Center, The Ohio State University, Columbus, Ohio 43210, United States.
Kathryn LoftisUnited States Army Futures Command DEVCOM Analysis Center, Aberdeen Proving Ground, Maryland, 21005, United States.
Jun ZhangMedical Physics, The Ohio State University Wexner Medical Center, Columbus, Ohio 43210, United States.
Lauren E HaydenInjury Biomechanics Research Center, The Ohio State University, Columbus, Ohio 43210, United States.
Randee L HunterInjury Biomechanics Research Center, The Ohio State University, Columbus, Ohio 43210, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Volumetric bone mineral density (vBMD) is commonly assessed using QCT. Although standard vBMD calculation methods require phantom rods that may not be available, internal-reference phantomless (IPL) and direct measurements of Hounsfield units (HU) can be used to calculate vBMD in their absence. Yet, neither approach has been systemically assessed across skeletal sites, and HU need further validation as a vBMD proxy. This study evaluated the accuracy of phantomless methods, including IPL and regression-based phantomless (RPL) calibration using HU to calculate vBMD, compared to phantom-based (PB) methods. vBMD from QCT scans of 100 male post-mortem human subjects (PMHS) was calculated using site-specific PB calibration at multiple skeletal sites throughout the body. A development sample of 50/100 PMHS was used to determine site-specific reference material density for IPL calibration and RPL equations. Reference densities and equations from the development sample were used to calculate IPL and RPL vBMD on the remaining 50/100 PMHS for method validation. PB and IPL/RPL vBMD were not significantly different (

Indexed as

bone qualityinternal reference calibrationphantomless calibrationQCTregression-based calibrationvBMD

Identifiers

PMID39224571
PMCPMC11366047

What OpenQuestion holds

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LicenceCC BY-NC
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.