ArticleData in brief2021
Article in Data in brief, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Evaluation of structured training in osteotomy and splitting for BSSO using a 3D-printed mandibular model.BMC medical education · 2026Article
- Automated skull orientation classification in Thai population using CNN-based transfer learning.Scientific reports · 2026Article
- Article
- A Comprehensive AI Framework for Superior Diagnosis, Cranial Reconstruction, and Implant Generation for Diverse Cranial Defects.Bioengineering (Basel, Switzerland) · 2025Article
- A novel approach to craniofacial analysis using automated 3D landmarking of the skull.Scientific reports · 2024Article
- Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model.Journal of medical systems · 2024Article
- Automated 3D Landmarking of the Skull: A Novel Approach for Craniofacial Analysis.bioRxiv : the preprint server for biology · 2024Article
- Creating high-resolution 3D cranial implant geometry using deep learning techniques.Frontiers in bioengineering and biotechnology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
In this article, we present a skull database containing 500 healthy skulls segmented from high-resolution head computed-tomography (CT) scans and 29 defective skulls segmented from craniotomy head CTs. Each healthy skull contains the complete anatomical structures of human skulls, including the cranial bones, facial bones and other subtle structures. For each craniotomy skull, a part of the cranial bone is missing, leaving a defect on the skull. The defects have various sizes, shapes and positions, depending on the specific pathological conditions of each patient. Along with each craniotomy skull, a cranial implant, which is designed manually by an expert and can fit with the defect, is provided. Considering the large volume of the healthy skull collection, the dataset can be used to study the geometry/shape variabilities of human skulls and create a robust statistical model of the shape of human skulls, which can be used for various tasks such as cranial implant design. The craniotomy collection can serve as an evaluation set for automatic cranial implant design algorithms.
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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.