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Record W2189062991 · doi:10.1302/0301-620x.89b4.17893

The accuracy of image-free computer navigation in the placement of the femoral component of the Birmingham Hip Resurfacing

2007· article· en· W2189062991 on OpenAlexaff
Edward T. Davis, Price Gallie, Kelly Macgroarty, James P. Waddell, Emil H. Schemitsch

Bibliographic record

VenueJournal of Bone and Joint Surgery - British Volume · 2007
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsHip resurfacingComponent (thermodynamics)Computer scienceMedicineOrthodonticsArtificial intelligenceComputer visionSurgeryArthroplastyPhysics

Abstract

fetched live from OpenAlex

A cadaver study using six pairs of lower limbs was conducted to investigate the accuracy of computer navigation and standard instrumentation for the placement of the Birmingham Hip Resurfacing femoral component. The aim was to place all the femoral components with a stem-shaft angle of 135 degrees . The mean stem-shaft angle obtained in the standard instrumentation group was 127.7 degrees (120 degrees to 132 degrees ), compared with 133.3 degrees (131 degrees to 139 degrees ) in the computer navigation group (p = 0.03). The scatter obtained with computer-assisted navigation was approximately half that found using the conventional jig. Computer navigation was more accurate and more consistent in its placement of the femoral component than standard instrumentation. We suggest that image-free computer-assisted navigation may have an application in aligning the femoral component during hip resurfacing.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
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)0.0010.000

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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

Citations49
Published2007
Admission routes1
Has abstractyes

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