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Regional variations in cartilage thickness of the radial head; implications for prosthesis design

2013· article· en· W3171227749 on OpenAlexaff
Celine Yeung, Simon Deluce, Ryan Willing, Marjorie Johnson, Graham J.W. King, George S. Athwal

Bibliographic record

VenueThe FASEB Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsSt Joseph's Health CentreWestern University
Fundersnot available
KeywordsCadaveric spasmCartilageAnatomyArticular cartilageElbowRadial headRADIUSCircumferenceMaterials scienceMedicineBiomedical engineeringOsteoarthritisGeometryMathematicsComputer science

Abstract

fetched live from OpenAlex

Background Knowledge of potential variations in cartilage thickness is important in designing radial head implants. Objective To characterize the regional variations in radial head cartilage thickness. Methods Twenty‐seven cadaveric radii were dissected and scanned with computed tomography (CT) in neutral position. Three dimensional models were generated from CT scans and processed through a computer program by two independent observers. Cartilage thickness values were obtained at 41 predetermined landmarks located around the articular dish and side of the radial head. Results At the side of the radial head, cartilage in the posteromedial quadrant (0° to 90°) was significantly thicker than all other quadrants (p < 0.05). Cartilage thickness measurements within the articular dish were similar at a mean of 0.98 ± 0.13mm, but increased towards the rim of the radial head (p < 0.001). Regional variations of cartilage thickness within the rim circumference were also detected, with the thickest region located anteriorly (270°, p < 0.001) and thinnest region laterally (180°, p < 0.05). Conclusions Regional variations in cartilage thickness exist around the side of the radial head and within the articular dish. Such differences offer insight into the biomechanics of the elbow joint and may also affect overall radius length and dish depth, which may be clinically important for implant design. Funding: Departmental Grant Funding Source : Departmental

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.179

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 head0.065
GPT teacher head0.294
Teacher spread0.229 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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