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Record W2746632766 · doi:10.1111/1556-4029.13627

The Accuracy and Applicability of 3D Modeling and Printing Blunt Force Cranial Injuries

2017· article· en· W2746632766 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Forensic Sciences · 2017
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBluntScannerPhotogrammetryLaser scanningCranial bone3d printedOrthodonticsHingeMedicineBiomedical engineeringSurgeryComputer scienceStructural engineeringEngineeringLaserSkullArtificial intelligenceOptics

Abstract

fetched live from OpenAlex

Abstract The purpose of this study was to determine the factors affecting the accuracy of 3D models and 3D prints of cranial blunt force trauma, to evaluate the applicability and limitations of modeling such injuries. Three types of cranial blunt force lesions were documented (hinge, depressed, and comminuted) using three forms of surface scanning (laser, structured light scanner, and photogrammetry) at two different quality settings (standard and high). 3D printed models of the lesions were produced using two different materials (a gypsum‐like composite powder called VisiJet ® PXL and an acrylic engineered composite plastic called VisiJet ® M3 in crystal colour). The results of these analyzes indicate the prints in this study exhibit some statistically significant differences from the actual bone lesions, but details of the lesions can be reproduced to within 2 mm accuracy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.287
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it