ReviewAnnals of translational medicine2021
Point of care virtual surgical planning and 3D printing in facial gender confirmation surgery: a narrative review.
Review in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed, 25 citations in OpenAlex.
- A step-by-step guide for implementation of virtual surgical applications and patient data centralization: practical experience and lessons learned.3D printing in medicine · 2026Review
- Appropriate Use of Preoperative Imaging in Feminization Cranioplasty.Craniomaxillofacial trauma & reconstruction · 2024Article
- 3D Printing Applications for Craniomaxillofacial Reconstruction: A Sweeping Review.ACS biomaterials science & engineering · 2023Review
- Development and evaluation of a portable and soft 3D-printed cast for laparoscopic choledochojejunostomy model in surgical training.BMC medical education · 2023Article
- Establishing a Point-of-Care Virtual Planning and 3D Printing Program.Seminars in plastic surgery · 2022Review
- Point of Care Virtual Surgical Planning and 3D Printing in Facial Feminization Surgery.Seminars in plastic surgery · 2022Review
- Facial Feminization Surgery: A Systematic Review of Perioperative Surgical Planning and Outcomes.Plastic and reconstructive surgery. Global open · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Facial gender confirmation surgery (FGCS) is a powerful set of procedures in the armamentarium of plastic surgeons that can transform the male face into a gender-congruent female face and provide the transgender individual with improved quality of life, positive body image and help in social integration. The goals of the FGCS procedures are to address the individual patients' concerns and expectations about their facial appearance, offer safely executed surgery, minimize complications, and optimize surgical outcomes. Pre-operative computed tomography (CT) scanning and three-dimensional (3D) reconstruction before facial feminization or masculinization delineates important skeletal and sinus anatomy and can also be a useful tool in patient consultation. Virtual surgical planning (VSP) is a valuable tool in facial surgery. From free flap bony reconstruction after tumor resection and orthognathic surgery to craniosynostosis planning, VSP has become widely utilized in modern day cranio-maxillofacial surgery. The use of patient-specific cutting guides and implants helps in improving symmetry and safety of these procedures. Furthermore, 3D printed models are valuable tools in patient education and counseling prior to surgery. In this article we describe our approach to FGCS through the integration of point of care (POC) VSP and 3D printing (3DP) to help deliver safer and accurate FGCS outcomes.
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Registered trials
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.