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Record W1999074106 · doi:10.1007/s11999-008-0299-z

Femoral Component Positioning in Hip Resurfacing With and Without Navigation

2008· article· en· W1999074106 on OpenAlexaff
Muthu Ganapathi, Pascal‐André Vendittoli, Martin Lavigne, Klaus‐Peter Günther

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2008
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineHip resurfacingComponent (thermodynamics)Orthopedic surgerySports medicineArthroplastySurgeryPhysical medicine and rehabilitationPhysical therapy

Abstract

fetched live from OpenAlex

UNLABELLED: Early failures after hip resurfacing often are the result of technical errors in placing the femoral component. We asked whether image-free computer navigation decreased the number of outliers compared with the conventional nonnavigated technique. We retrospectively compared 51 consecutive hip resurfacings performed using image-free computer navigation with 88 consecutive hip resurfacings performed without navigation. Patient demographics were similar. There were no differences in the average native femoral neck-shaft angles, planned stem-shaft angles, or postoperative stem-shaft angles. However, when the postoperative stem-shaft angle was compared with the planned stem-shaft angle, there were 33 patients (38%) in the nonnavigated group with a deviation greater than 5 degrees in contrast to none in the navigated group. Notching was present in four patients in the nonnavigated group and none in the navigated group. The average operative time was 111 minutes for the navigated group and 105 minutes for the nonnavigated group. Image-free navigation decreased the number of patients with potentially undesirable implant placements. LEVEL OF EVIDENCE: Level III, therapeutic study. See the Guidelines for Authors for a complete description of levels of evidence.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.106
GPT teacher head0.408
Teacher spread0.303 · 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

Citations54
Published2008
Admission routes1
Has abstractyes

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