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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 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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.005
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.004

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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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