Evidence map›Paper›PMID 37416976›Full record

ArticlePediatrics2023

Synthetic CT Assessment of Lesions in Children With Rare Musculoskeletal Diseases.

Jaymin Upadhyay, Jade Iwasaka-Neder, Emma Golden, Sarah Bixby

Abstract readCase Reports
In one paragraph

Article in Pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Observational
  3. Review
  4. Review
  5. 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

4 authors.

Jaymin Upadhyay *Departments of Anesthesiology, Critical Care and Pain Medicine.
Jade Iwasaka-NederRadiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts.
Emma GoldenDepartments of Anesthesiology, Critical Care and Pain Medicine.
Sarah Bixby *Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts.

Funding

Acquisition of a Siemens 3T MRI for Research ImagingS10OD025111 · OD · BOSTON CHILDREN'S HOSPITAL · PI WARFIELD, SIMON K · 2018 to 2018
$2.0M
NIH HHS S10 OD025111
6 · The paper itself

Abstract

Imaging modalities such as computed tomography (CT) are critical for monitoring musculoskeletal abnormalities in children with rare diseases. However, CT exposes patients to radiation, which limits its utility in the clinical setting, particularly during longitudinal evaluation. Synthetic CT is a novel, noncontrast, and rapid MRI method that can provide CT-like images without any radiation exposure and is easily performed in conjunction with traditional MRI, which detects soft-tissue and bone marrow abnormalities. To date, an evaluation of synthetic CT in pediatric patients with rare musculoskeletal diseases has been lacking. In this case series, the capability of synthetic CT to identify musculoskeletal lesions accurately in 2 rare disease patients is revealed. In Case 1, synthetic CT, in agreement with routine CT, identified an intraosseous lesion in the right femoral neck in a 16-year-old female with fibrous dysplasia, whereas standard-of-care MRIs additionally revealed mild surrounding edema-like bone marrow signal. For Case 2, synthetic CT applied to a 12-year-old female with fibrodysplasia ossificans progressiva revealed heterotopic ossification present along the cervical spine that had caused the fusion of multiple vertebrae. Our evaluation of synthetic CT offers important insights into the feasibility and utility of this methodology in children with rare diseases affecting the musculoskeletal system.

Indexed as

Myositis OssificansOssification, HeterotopicAdolescentChildFemaleHumansMagnetic Resonance ImagingRare DiseasesTomography, X-Ray Computed

Identifiers

PMID37416976
PMCPMC10389770

What OpenQuestion holds

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Registered trials

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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.