Evidence map›Paper›PMID 39118860›Full record

ReviewSeminars in plastic surgery2024

Application of Computerized Surgical Planning in Craniosynostosis Surgery.

Neil Parikh, Ali Aral, Katelyn Lewis, Michael Alperovich

Abstract readReview
In one paragraph

Review in Seminars in plastic surgery, 2024. 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
–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

2 citing papers in PubMed.

  1. Review
  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.

Neil ParikhDivision of Plastic and Reconstructive Surgery, Department of Surgery, Yale School of Medicine, New Haven, Connecticut.
Ali AralDivision of Plastic and Reconstructive Surgery, Department of Surgery, Yale School of Medicine, New Haven, Connecticut.
Katelyn LewisDivision of Plastic and Reconstructive Surgery, Department of Surgery, Yale School of Medicine, New Haven, Connecticut.
Michael AlperovichDivision of Plastic and Reconstructive Surgery, Department of Surgery, Yale School of Medicine, New Haven, Connecticut.

Funding

Institutional Career Development CoreKL2TR001862 · NCATS · YALE UNIVERSITY · PI CANTLEY, LLOYD G, EDELMAN, E. JENNIFER · 2016 to 2025
$12.2M
NCATS NIH HHS KL2 TR001862
6 · The paper itself

Abstract

Craniosynostosis, a medical condition characterized by premature fusion of one or multiple cranial sutures, has historically been treated through surgical correction. Computerized Surgical Planning (CSP) and three-dimensional (3D) modeling have gained significant popularity across craniofacial surgery. Through a collaborative effort between surgeons and engineers, it is now possible to virtually execute a surgical plan based on preoperative imaging using computed tomography scans. The CSP workflow involves several elements including virtual 3D modeling, CSP computer-aided surgical guide design, manufacturing of guides and templates, and intraoperative implementation. Through the gradual optimization of this workflow, it has been possible to achieve significant progress in the surgical process including improvements in the preoperative planning of complex craniosynostosis cases and reduction of intraoperative time. Furthermore, CSP and 3D modeling have had a positive impact on surgical simulation and residency training, along with patient education and counseling. This article summarizes the CSP workflow in the treatment of craniosynostosis and the implications of this treatment modality on medical trainee education and patient management.

Indexed as

normative pediatric skull modelovercorrectionresorbable plating

Identifiers

PMID39118860
PMCPMC11305832

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.