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Record W2133834211 · doi:10.1177/2325967114541414

Diagnosing Femoroacetabular Impingement From Plain Radiographs

2014· article· en· W2133834211 on OpenAlexaff
Olufemi R. Ayeni, Kevin Chan, Daniel B. Whelan, Rajiv Gandhi, Dale Williams, Srinivasan Harish, Hema Choudur, Mary M. Chiavaras, Jón Karlsson, Mohit Bhandari

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsSt. Michael's HospitalUniversity of TorontoMcMaster University
Fundersnot available
KeywordsFemoroacetabular impingementMedicineIntraclass correlationRadiographyRadiologyOrthopedic surgeryNuclear medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: A diagnosis of femoroacetabular impingement (FAI) requires careful history and physical examination, as well as an accurate and reliable radiologic evaluation using plain radiographs as a screening modality. Radiographic markers in the diagnosis of FAI are numerous and not fully validated. In particular, reliability in their assessment across health care providers is unclear. PURPOSE: To determine inter- and intraobserver reliability between orthopaedic surgeons and musculoskeletal radiologists. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 3. METHODS: Six physicians (3 orthopaedic surgeons, 3 musculoskeletal radiologists) independently evaluated a broad spectrum of FAI pathologies across 51 hip radiographs on 2 occasions separated by at least 4 weeks. Reviewers used 8 common criteria to diagnose FAI, including (1) pistol-grip deformity, (2) size of alpha angle, (3) femoral head-neck offset, (4) posterior wall sign abnormality, (5) ischial spine sign abnormality, (6) coxa profunda abnormality, (7) crossover sign abnormality, and (8) acetabular protrusion. Agreement was calculated using the intraclass correlation coefficient (ICC). RESULTS: When establishing an FAI diagnosis, there was poor interobserver reliability between the surgeons and radiologists (ICC batch 1 = 0.33; ICC batch 2 = 0.15). In contrast, there was higher interobserver reliability within each specialty, ranging from fair to good (surgeons: ICC batch 1 = 0.72; ICC batch 2 = 0.70 vs radiologists: ICC batch 1 = 0.59; ICC batch 2 = 0.74). Orthopaedic surgeons had the highest interobserver reliability when identifying pistol-grip deformities (ICC = 0.81) or abnormal alpha angles (ICC = 0.81). Similarly, radiologists had the highest agreement for detecting pistol-grip deformities (ICC = 0.75). CONCLUSION: These results suggest that surgeons and radiologists agree among themselves, but there is a need to improve the reliability of radiographic interpretations for FAI between the 2 specialties. The observed degree of low reliability may ultimately lead to missed, delayed, or inappropriate treatments for patients with symptomatic FAI.

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)0.014
metaresearch head score (Gemma)0.057
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 score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.057
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.002
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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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