MétaCan
Menu
Back to cohort
Record W4297021829 · doi:10.1055/s-0042-1756517

Accuracy of the Surface Contour of Three-Dimensional-Printed Canine Pelvic Replicas

2022· article· en· W4297021829 on OpenAlexaff
Ming Lu, Griselda Lam, Junemoe Jeong, S. Kim

Bibliographic record

VenueVeterinary and Comparative Orthopaedics and Traumatology · 2022
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsVictoria Hospital
Fundersnot available
KeywordsCadaveric spasmMedicineContouringAcetabulum3d printedPelvisOrthodonticsMean squared errorSurface (topology)AnatomyNuclear medicineBiomedical engineeringGeometryMathematicsComputer scienceComputer graphics (images)Statistics

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to determine the differences in surface contour between models of native pelvic bones and their corresponding three-dimensional (3D)-printed replicas. STUDY DESIGN: Digital 3D models of five cadaveric hemipelves and five live dogs with contralateral pelvic fractures were generated based on computed tomographic images and 3D printed. The 3D-printed replicas underwent 3D scanning and digital 3D models of the replicas were created. The digital 3D model of each replica was superimposed onto the model of the native hemipelvis. Errors in the replicas were determined by comparing the distances of 120,000 corresponding surface points between models. The medial surface, lateral surface and dorsal surface of the acetabulum (DSA) of each hemipelvis were selected for further analysis. The root mean square error (RMSE) was compared between various selected areas using a one-way repeated measures analysis of variance, followed by a Bonferroni post-hoc test. RESULTS: < 0.001). CONCLUSION: The 3D-printed replicas were adequate in serving as a template for the pre-contouring of bone plates in fracture repair of pelvic fractures, particularly those that demand accurate reduction such as acetabular fractures.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.066
GPT teacher head0.310
Teacher spread0.244 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueVeterinary and Comparative Orthopaedics and TraumatologySame topicPelvic and Acetabular InjuriesFrench-language works237,207