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Record W2948341437 · doi:10.1055/s-0039-1683967

Femoroacetabular Impingement: What the Surgeon Wants to Know

2019· review· en· W2948341437 on OpenAlexaff
Paulo Rego, Paul E. Beaulé, Olufemi R. Ayeni, Marc Tey, Óliver Marín-Peña, Pedro Dantas, Geoffrey Wilkin, George Grammatopoulos, Inês Mafra, Kevin Smit, Adrian Z. Kurz

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2019
Typereview
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsChildren's Hospital of Eastern OntarioMcMaster University Medical CentreOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsFemoroacetabular impingementMedicineAbnormalityArticular cartilageOrthopedic surgeryRadiologySurgeryOsteoarthritisPathology

Abstract

fetched live from OpenAlex

Femoroacetabular impingement (FAI) is increasingly recognized as a risk factor for early hip degeneration in young active patients. The diagnosis depends on clinical examination and proper imaging that should be able to identify abnormal and sometimes subtle morphological changes. Labral tears and cartilage lesions rarely occur without underlying bone abnormalities. Surgical approaches to treat FAI are increasing significantly worldwide, even without a clearly defined consensus of what should be accepted as the standard imaging diagnosis for FAI morphology.Hip abnormalities encompass many variations related to the shape, size, and spatial orientation of both sides of the joint and can be difficult to characterize if adequate imaging is not available.This article presents a comprehensive review about the information orthopaedic surgeons need to know from radiologists to plan the most rational approach to a painful hip resulting from a mechanical abnormality.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.354
Teacher spread0.327 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations15
Published2019
Admission routes1
Has abstractyes

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