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Record W2412114569 · doi:10.1503/cjs.011415

Accuracy of the modified Hardinge approach in acetabular positioning

2016· article· en· W2412114569 on OpenAlexaffvenue
Prateek Goyal, Adrian Lau, Richard W. McCalden, Matthew G. Teeter, James L. Howard, Brent A. Lanting

Bibliographic record

VenueCanadian Journal of Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsMedicineTotal hip arthroplastyRadiographyOrthodonticsArthroplastySurgeryFixation (population genetics)Nuclear medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The surgical approach chosen for total hip arthroplasty (THA) may affect the positioning of the acetabular component. The purpose of this study was to examine the accuracy in orienting the acetabular component using the modified Hardinge approach. METHODS: We used our institutional arthroplasty database to identify patients with primary, press-fit, hemispherical acetabular components of a metal-on-polyethylene THA performed between 2003 and 2011. Patients with radiographs obtained 1-3 years after the index procedure were included for measurement of anteversion and inclination angles. Acceptable values of anteversion and abduction angles were defined as 15° ± 10° and 40° ± 10°, respectively. RESULTS: We identified 1241 patients from the database, and the modified Hardinge approach was used in 1010 of the patients included in our analysis. The acetabular component was anteverted in the acceptable zone in 54.1% of patients. The abduction angle was within the defined range in 79.2% of patients. Combined anteversion and abduction angles within the defined zone were present in 43.6% of patients. CONCLUSION: Consistent with studies examining accuracy from other approaches, our study reveals that the modified Hardinge approach was only moderately accurate in positioning the acetabular component in the acceptable zone.

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)0.001
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 score0.050
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.040
GPT teacher head0.237
Teacher spread0.197 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

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