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Record W2977861770 · doi:10.29173/aar62

Regional Symmetry of the Pelvis

2019· article· en· W2977861770 on OpenAlexafffundvenue
Sarah E. McClelland, Samantha Polege, David Li, Maha Ead, Lindsey Westover, Kansas Duke

Bibliographic record

VenueAlberta Academic Review · 2019
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsUniversity of Alberta
FundersSyncrude
KeywordsPelvisProcess (computing)AnatomyComputer scienceAcetabulumSymmetry (geometry)AsymmetryGeologyOrthodonticsMathematicsPhysicsGeometryMedicine

Abstract

fetched live from OpenAlex

Understanding the left-right symmetry of the pelvis can assist with the virtual reconstruction of fractured pelvises in the future. Further specifying the different regions can give more information regarding the anatomy of the bone. CT scans of intact pelvises are turned into 3D models using a computer program called Mimics. The femurs and spine are removed manually to isolate the bone. The model can then be analyzed and the regions defined. The pelvis is split into three pieces using the program Geomagic, the iliac, acetabular, and pubic rami regions. The two cuts are made based around the highest and lowest points of the acetabular cup. From here each cut piece can be mirrored and aligned with the opposing side of the pelvis. Colour deviation maps can then be generated to visualize the asymmetry, as well as the RMS value and the percentage of points within a 2 mm deviation threshold. The average RMS values are below 2 mm and the percentage of points within 2 mm is high. My research reflects that the pelvis is symmetrical and may be used to assist in the surgical planning process of pelvic 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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.444
Threshold uncertainty score0.959

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)0.0010.001

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 head0.302
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2019
Admission routes3
Has abstractyes

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