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Record W4286586603 · doi:10.1177/03635465221109240

Comparison of Acetabular Measurements Between 2 Validated Software Programs Used in Hip Preservation Surgery

2022· article· en· W4286586603 on OpenAlexaff
Pierre Laboudie, Daniel Fischman, Andrew Speirs, Saif Salih, Fernando Holc, Paul E. Beaulé, Johan Witt, George Grammatopoulos

Bibliographic record

VenueThe American Journal of Sports Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsCarleton UniversityOttawa Hospital
Fundersnot available
KeywordsAcetabulumMedicinePelvic tiltNuclear medicineRadiographyFemoral headPelvisOrthodonticsRadiologySurgery

Abstract

fetched live from OpenAlex

Background: Validated software tools (Clinical Graphics [CG] and Hip 2 Norm) permit measurement of the percentage of femoral head coverage (%FHC), which aids in morphological classification and prediction of outcome after hip preservation surgery. Purpose: (1) To assess whether acetabular parameter measurements determined from 2 commonly used software systems are comparable. (2) To determine which parameters influence the correlation or differences between software outputs and measurements. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: The study included 69 patients (90 hips) who underwent periacetabular osteotomy and had comprehensive preoperative imaging available. Lateral center-edge angle (LCEA), acetabular index (AI), and %FHC were determined using 3-dimensional computed tomography (CT) measurements by CG and Hip 2 Norm software. Images of 18 pelvises were segmented to determine spinopelvic parameters and subtended acetabular angles. Between-group measurements were compared using correlation coefficients and Bland-Altman analyses. The difference in the outputs of the 2 programs was defined as delta (Δ). Radiographic parameters were tested to assess whether they were responsible for differences in %FHC between software programs. Results: Strong correlations between LCEA (ρ = 0.862) and AI (ρ = 0.825) measurements were seen between the Hip 2 Norm and CG programs. However, weak correlation was seen in the estimate of %FHC (ρ = 0.358), with the presence of a systematic error. Hip 2 Norm consistently produced lower anterior, posterior, and total %FHC values than CG. The %FHC determined by CG, but not Hip 2 Norm, correlated with acetabular subtended angles ( P < .05). Pelvic tilt measured on CT did not correlate with pelvic tilt estimated by Hip 2 Norm ( P = .56), and ΔPelvicTilt strongly correlated with the difference in %FHC by the 2 software programs (ρ = 0.63; P = .005), pelvic incidence (ρ = 0.73; P < .001), and pelvic tilt (ρ = −0.91; P < .001) as per CT. Conclusion: The correlation of %FHC between Hip 2 Norm and CG was weak (ρ = 0.358). The difference in measurements of %FHC correlated with ΔPelvicTilt. The %FHC determined by CG strongly correlated with the segmented acetabular subtended angles and thus more likely reflected true values. Hip preservation surgeons should be aware of these measurement differences because %FHC is important in the diagnosis and prognosis of acetabular dysplasia.

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)0.006
metaresearch head score (Gemma)0.034
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 score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.111
GPT teacher head0.342
Teacher spread0.231 · 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".

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Citations6
Published2022
Admission routes1
Has abstractyes

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