Evidence map›Paper›PMID 38397270›Full record

ArticleChildren (Basel, Switzerland)2024

3D Back Contour Metrics in Predicting Idiopathic Scoliosis Progression: Retrospective Cohort Analysis, Case Series Report and Proof of Concept.

Milan Patel, Xue-Cheng Liu, Kai Yang, Channing Tassone, Benjamin Escott, John Thometz

Abstract read
In one paragraph

Article in Children (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 3 pooled it
–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, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  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

6 authors.

Milan PatelDepartment of Orthopedic Surgery, Children's Wisconsin, Medical College of Wisconsin, Greenfield, WI 53227, USA.
Xue-Cheng LiuDepartment of Orthopedic Surgery, Children's Wisconsin, Medical College of Wisconsin, Greenfield, WI 53227, USA.ORCID 0000-0003-0009-0555
Kai YangDivision of Biostatistics, Institute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Channing TassoneDepartment of Orthopedic Surgery, Children's Wisconsin, Medical College of Wisconsin, Greenfield, WI 53227, USA.
Benjamin EscottDepartment of Orthopedic Surgery, Children's Wisconsin, Medical College of Wisconsin, Greenfield, WI 53227, USA.
John ThometzDepartment of Orthopedic Surgery, Children's Wisconsin, Medical College of Wisconsin, Greenfield, WI 53227, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adolescent Idiopathic Scoliosis is a 3D spinal deformity commonly characterized by serial radiographs. Patients with AIS may have increased average radiation exposure compared to unaffected patients and thus may be implicated with a modest increase in cancer risk. To minimize lifetime radiation exposure, alternative imaging modalities such as surface topography are being explored. Surface topography (ST) uses a camera to map anatomic landmarks of the spine and contours of the back to create software-generated spine models. ST has previously shown good correlation to radiographic measures. In this study, we sought to use ST in the creation of a risk stratification model. A total of 38 patients met the inclusion criteria for curve progression prediction. Scoliotic curves were classified as progressing, stabilized, or improving, and a predictive model was created using the proportional odds logistic modeling. The results showed that surface topography was able to moderately appraise scoliosis curvatures when compared to radiographs. The predictive model, using demographic and surface topography measurements, was able to account for 86.9% of the variability in the future Cobb angle. Additionally, attempts at classification of curve progression, stabilization, or improvement were accurately predicted 27/38 times, 71%. These results provide a basis for the creation of a clinical tool in the tracking and prediction of scoliosis progression in order to reduce the number of X-rays required.

Indexed as

adolescent idiopathic scoliosispredictive modelingradiographysurface topography

Identifiers

PMID38397270
PMCPMC10886742

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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.