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Record W4400269458 · doi:10.1016/j.jseint.2024.06.009

Inter-rater and intrarater reliability of superior labrum anterior to posterior lesion classification using magnetic resonance arthrography

2024· article· es· W4400269458 on OpenAlexaff
Austin W. Bowering, Brittany N. Bolt, Conall G. Donaghy, Nicholas Smith

Bibliographic record

VenueJSES International · 2024
Typearticle
Languagees
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMagnetic resonance imagingLabrumIntra-rater reliabilityMedicineLesionReliability (semiconductor)AnatomyRadiologySurgeryPhysicsArthroscopyInternal medicine

Abstract

fetched live from OpenAlex

BackgroundThe glenoid labrum is a fibrocartilaginous ring that affixes the joint capsule and ligaments of the glenohumeral joint. Superior labrum anterior to posterior (SLAP) lesions are a subset of injuries that affect the superior glenoid labrum, most common in laborers and overhead-throwing athletes. In 1990, Snyder et al classified SLAP lesions into one of four types. Later, Maffet et al expanded this scale to include three additional subclassifications. At present, arthroscopy is considered the gold standard for SLAP tear diagnosis. Classification under arthroscopy has demonstrated low to moderate inter-rater reliability. Magnetic resonance arthrography (MRa) is an alternate, less invasive test for diagnosing SLAP lesions. The reliability of MRa for diagnosing slap tears is uncertain.MethodsMagnetic resonance arthrograms were identified using the Picture Archiving and Communication System (PACS). In total, 273 shoulder arthrograms were reviewed, and 20 were selected with the desired pathology. Three orthopedic surgeons and three musculoskeletal radiologists were asked to classify the SLAP lesions into one of seven categories (Snyder & Maffet classification systems). Data was collected on two separate occasions at an interval of at least two months. Inter-rater and intrarater reliability were calculated using Fleiss Kappa and Cohen's Kappa, respectively.ResultsBetween all raters, there was poor inter-rater reliability for each round of data collection (κ = .177, κ = .124 for rounds 1 and 2, respectively). Between orthopedic surgeons, there were poor levels of agreement (κ = −.056, κ = .114), whereas, between radiologists, there was fair to moderate agreement (κ = 0.479, κ = 0.340). Within orthopedic raters, κ values ranged from −0.059 to 0.125, indicating, at best, poor intrarater reliability. Within radiologists, κ values ranged from 0.545 to 0.553, indicating moderate agreement within raters. The analysis determined that none of the orthopedic values for inter or intrarater reliability could be deemed statistically different from zero.ConclusionOverall, classification using MRa resulted in significant disagreement between and within raters. Trained radiologists demonstrated higher overall levels of agreement than orthopedic surgeons. In summary, when using MRa to assess SLAP lesions, Snyder and Maffet classification demonstrates poor reliability by orthopedic surgeons and moderate reliability when used by musculoskeletal radiologists. The glenoid labrum is a fibrocartilaginous ring that affixes the joint capsule and ligaments of the glenohumeral joint. Superior labrum anterior to posterior (SLAP) lesions are a subset of injuries that affect the superior glenoid labrum, most common in laborers and overhead-throwing athletes. In 1990, Snyder et al classified SLAP lesions into one of four types. Later, Maffet et al expanded this scale to include three additional subclassifications. At present, arthroscopy is considered the gold standard for SLAP tear diagnosis. Classification under arthroscopy has demonstrated low to moderate inter-rater reliability. Magnetic resonance arthrography (MRa) is an alternate, less invasive test for diagnosing SLAP lesions. The reliability of MRa for diagnosing slap tears is uncertain. Magnetic resonance arthrograms were identified using the Picture Archiving and Communication System (PACS). In total, 273 shoulder arthrograms were reviewed, and 20 were selected with the desired pathology. Three orthopedic surgeons and three musculoskeletal radiologists were asked to classify the SLAP lesions into one of seven categories (Snyder & Maffet classification systems). Data was collected on two separate occasions at an interval of at least two months. Inter-rater and intrarater reliability were calculated using Fleiss Kappa and Cohen's Kappa, respectively. Between all raters, there was poor inter-rater reliability for each round of data collection (κ = .177, κ = .124 for rounds 1 and 2, respectively). Between orthopedic surgeons, there were poor levels of agreement (κ = −.056, κ = .114), whereas, between radiologists, there was fair to moderate agreement (κ = 0.479, κ = 0.340). Within orthopedic raters, κ values ranged from −0.059 to 0.125, indicating, at best, poor intrarater reliability. Within radiologists, κ values ranged from 0.545 to 0.553, indicating moderate agreement within raters. The analysis determined that none of the orthopedic values for inter or intrarater reliability could be deemed statistically different from zero. Overall, classification using MRa resulted in significant disagreement between and within raters. Trained radiologists demonstrated higher overall levels of agreement than orthopedic surgeons. In summary, when using MRa to assess SLAP lesions, Snyder and Maffet classification demonstrates poor reliability by orthopedic surgeons and moderate reliability when used by musculoskeletal radiologists.

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)0.066
metaresearch head score (Gemma)0.103
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 score0.066
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.103
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.034
GPT teacher head0.349
Teacher spread0.315 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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