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Record W4405557555 · doi:10.4055/cios24061

Posterior Acetabular Coverage of the Femoral Head in Sport-Related Posterior Hip Dislocation or Subluxation

2024· article· en· W4405557555 on OpenAlexaff
Hyung-Ku Yoon, Gi-Ho Moon, Joosuk Ahn, Kyu Hyun Yang

Bibliographic record

VenueClinics in Orthopedic Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsSt. Peter's Hospital
Fundersnot available
KeywordsMedicineFemoral headSubluxationDislocationAcetabulumOrthodonticsSurgery

Abstract

fetched live from OpenAlex

Backgroud: Decreased acetabular version and posterior acetabular coverage of the femoral head have been regarded as the leading causes of sport-related posterior hip dislocation or subluxation. This study aimed to examine the posterior acetabular coverage of the femoral head in 21 patients who sustained posterior hip dislocation or subluxation during sport activities. Methods: The anterior and posterior acetabular rims on 3-dimensional computed tomography (3D-CT) images were delineated on the normal side in these patients. Radiologic signs, including crossover and posterior wall signs, were examined. The fracture center level (FCL) of the posterior acetabular wall was identified on axial CT images of the injured hip and the level was marked on the normal side. The difference in the femoral head coverage by posterior and anterior acetabular rims was measured by measuring the horizontal distance between anterior and posterior acetabular rims at the FCL (posterior-anterior [P-A] index). The acetabular version was measured at the femoral head and FCL using axial CT images of the normal side. Femoral head coverage by the posterior acetabular wall on the normal side was measured using 3D-CT (areal coverage). Results: = 0.003). Conclusions: Superior-posterior coverage of the femoral head by the posterior acetabular wall was insufficient in patients who sustained hip posterior dislocation or subluxation during sports activities.

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 score0.091
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.038
GPT teacher head0.337
Teacher spread0.299 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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