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Bone Remodeling is Different in Metaphyseal and Diaphyseal-fit Uncemented Hip Stems

2006· article· en· W2095127862 on OpenAlexaff
Jun Saito, Nadim Aslam, Kenji Tokunaga, Emil H. Schemitsch, James P. Waddell

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2006
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersSmith and Nephew
KeywordsMedicinePeriprostheticBone remodelingFemoral canalOrthopedic surgeryRadiographySurgeryCortical boneStress shieldingDentistryProsthesisArthroplastyImplantAnatomyInternal medicine

Abstract

fetched live from OpenAlex

Femoral component stability in uncemented total hip arthroplasties depends on periprosthetic bone remodeling. Stem design is an important factor influencing bone remodeling, however the design that promotes the most bone remodeling is unclear. We examined metaphyseal and diaphyseal-fit stems to determine the effect of stem design on bone remodeling and stability. Twenty-three patients who had total hip arthroplasties (28 hips) with metaphyseal-fit stems were matched with 27 patients (32 hips) who had uncemented total hip arthroplasties with diaphyseal-fit stems. We assessed preoperative radiographs for canal fill, canal shape, and bone quality. We then assessed postoperative radiographs for periprosthetic bone remodeling including spot welds, cortical hypertrophy, and pedestal formation. Patients were examined clinically using a modified Harris hip score. Patients with metaphyseal stems had increased cortical hypertrophy 1 year postoperatively. However, there was no functional difference 2 years postoperatively. Both stem designs resulted in bone remodeling by 2 years postoperatively with similar clinical results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.122
GPT teacher head0.418
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2006
Admission routes1
Has abstractyes

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