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Record W2324064609 · doi:10.1097/bco.0000000000000119

An analysis of the diagnostic measurements for assessing cam-type femoroacetabular impingement using computed tomography

2014· article· en· W2324064609 on OpenAlexaff
Andrew E. Giles, Sandeep Bhachu, N. Alex Corneman, John F. Rudan, Randy E. Ellis, Gavin Wood

Bibliographic record

VenueCurrent Orthopaedic Practice · 2014
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsFemoroacetabular impingementMedicineComputed tomographyOffset (computer science)RadiographyNuclear medicineRadiologyOsteoarthritisTomographyOblique case

Abstract

fetched live from OpenAlex

Background: Femoroacetabular impingement can lead to early osteoarthritis. Diagnosis is primarily radiographic relying on the alpha angle and the anterior offset ratio. There is no clear consensus on how to best diagnose impingement. Methods: This is a retrospective analysis using the alpha angle and anterior offset ratio in the oblique axial and radial planes of 81 preoperative CT scans of patients who underwent hip resurfacing. Results: There were no differences in average alpha angles between image planes. CT was validated for use of these measurements, and the anterior offset ratio was described for the first time in the radial plane using reformatted CT images. Conclusions: More precise methods may be needed to screen for femoracetabular impingent that address issues of sensitivity and variability.

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)0.005
metaresearch head score (Gemma)0.030
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 score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.080
GPT teacher head0.390
Teacher spread0.310 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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