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Record W3101869661 · doi:10.1002/rcs.2199

Development and application of the average pelvic shape in virtual pelvic fracture reconstruction

2020· article· en· W3101869661 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPelvic fractureComputer scienceOrthodonticsJoint (building)Fracture (geology)PelvisMedicineAnatomyGeologyStructural engineering

Abstract

fetched live from OpenAlex

BACKGROUND: With unilateral pelvic fractures, the contralateral hemipelvis can be used as a template in virtual reconstruction; however, this cannot be applied for bilateral fractures. Therefore, statistical shape modelling was used to build average pelvic shapes that can serve as templates when reconstructing bilaterally fractured pelvises. METHODS: Four average shape models were created for male and female, left and right hemipelves from 20 male and 20 female subjects. They were used as templates to reconstruct eight unilaterally fractured pelvises. RESULTS: The average root-mean-square of deviations between the reconstructed and intact hemipelves was 1.46 ± 0.32 mm, which is less than the 2 mm threshold for causing hip joint complications. CONCLUSION: This indicates that the reconstructions are reliable and the average shape models can be used to reconstruct bilaterally fractured pelvises. The proposed technique can potentially provide quick and accurate treatment plans for pelvic fracture patients, which is necessary for recovery.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
GPT teacher head0.259
Teacher spread0.242 · 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