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Record W1964301565 · doi:10.1097/blo.0b013e318137a13f

Hip Resurfacing Femoral Neck Fracture Influenced by Valgus Placement

2007· article· en· W1964301565 on OpenAlexaff
Carolyn Anglin, Bassam A. Masri, Jérôme Tonetti, Antony J. Hodgson, Nelson V. Greidanus

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2007
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of British Columbia
FundersZimmer
KeywordsValgusMedicineBone mineralFemoral neckCadaveric spasmFemurOrthodonticsFracture (geology)Bone densityArthroplastyOrthopedic surgeryDentistrySurgeryOsteoporosisInternal medicineGeology

Abstract

fetched live from OpenAlex

Femoral neck fracture is the most common short-term concern after hip resurfacing arthroplasty. Currently, there is little basis to decide between neutral and valgus placement. We loaded 10 notched cadaveric femur pairs to failure; one side was implanted at 0 degrees relative to the femoral neck and the other at 10 degrees valgus. All 20 were dual-energy X-ray absorptiometry-scanned. Failure load correlated with bone mineral density. Valgus placement increased the fracture load by an average of 28% over neutral for specimens with normal bone mineral density but had no effect on fracture load in specimens with low bone mineral density. For specimens with normal bone mineral density (typical of patients undergoing resurfacing arthroplasty), neutral-valgus placement had a greater effect than bone mineral density, explaining 54% of the fracture load variance. Component placement greater than 10 degrees valgus is likely undesirable because this can lead to an increase in component size and a greater likelihood of notching. To reduce fracture risk, we recommend placing the femoral component in valgus and selecting patients with higher bone mineral density.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.431
Teacher spread0.367 · 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

Citations90
Published2007
Admission routes1
Has abstractyes

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