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Record W1964823424 · doi:10.1177/2325967113s00092

Normal Glenoid Relationships Used for Unilateral Quantification of Glenoid Bone Loss in Glenohumeral Instability

2013· article· en· W1964823424 on OpenAlexaff
Aaron J. Bois, Alexander Rothy, Anish Ghodadra, Morgan H. Jones, Anthony Miniaci

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicShoulder and Clavicle Injuries
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineGlenoid cavityFossaArticular surfaceAnatomyOrthodonticsShoulder joint

Abstract

fetched live from OpenAlex

Objectives: Current methods used to quantify glenoid bone loss following anterior shoulder instability rely on bilateral shoulder imaging to obtain normal linear and surface area parameters of the uninjured glenoid fossa. This method is based on the assumption that there is little side-side variability in these anatomical relationships. Previous reports have demonstrated differences in the morphology of the glenoid fossa based on the anterior glenoid notch. The purpose of this study was to determine the normal dimensions of height, width, surface area, and shape of the human glenoid fossa as function of glenoid notch, and to determine if side-to-side differences exist for these parameters. Due to notch variation, we hypothesize that the inferior glenoid fossa is better represented as an ellipse versus a perfect circle as previously described. We also hypothesize that side-to-side differences exist in glenoid surface area. Methods: The authors studied 58 human scapulae pairs between 18 and 35 years of age from the Hamann-Todd Osteological Collection. Age, sex, race, body height and weight were known for each specimen. Paired specimens were sorted into groups of 5 according to race, sex, and notch type. All specimens were digitized using a 3-D laser scanner, with a stated accuracy of 0.005 inches. Height, width, surface area, and notch angle measurements were calculated using software written in the MATLAB platform. A best fit ellipse was applied to the inferior glenoid based on the glenoid circumference below the notch. Differences in surface area of paired glenoids were assessed using a matched pairs T-test. Multiple stepwise linear regression models were created to select predictors of glenoid surface area. Lastly, the intra-rater and inter-rater reliability of the notch classification as reported by Merrill et al. was assessed among 13 raters. Results: The mean height (s.d.) of the glenoid fossae was 35.0 ± 2.8 mm. Inferior width was 24.8 ± 2.5 mm. The best-fit ellipse of the inferior glenoid had a mean eccentricity of 0.425 ± 0.099. The right glenoid, when compared to its left counterpart, had greater overall surface area (x̄ right = 679.6 mm 2 , x̄ left = 657.2 mm 2 , P< 0.0001*) and inferior surface area (x̄ right = 548.2 mm 2 , x̄ left = 533.1 mm 2 , P< 0.0076*). Patient height, sex, and glenoid height correlated with total and inferior glenoid surface area with r 2 = 0.902 and 0.779, respectively (P< 0.0001*). Analysis of intra-observer reliability showed a consistency of 0.56 (95% CI= 0.26- 0.77), while the inter-observer reliability kappa coefficient was 0.43 (95% CI= 0.41- 0.45). Conclusion: By considering unilateral anatomic relationships of the glenoid fossa, we were able to determine alternative methods of evaluating glenoid bone loss. Glenoid notch angle had moderate reliability and was not considered clinically useful to stratify glenoid morphology. Based on non-zero eccentricity values of the best-fit ellipse, the inferior glenoid fossa did not represent a perfect circle. In addition, side-to-side differences were found between glenoid surface area measurements. The latter two findings contradict assumptions made by current techniques used in clinical practice to calculate bone loss and raise concern as to their validity. Using easily obtainable patient (height and sex) and glenoid (height) parameters, glenoid surface area can be predicted by means of regression modeling, permitting unilateral measurements of glenoid bone loss to be made in clinical practice.

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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)0.002
metaresearch head score (Gemma)0.006
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.325
Teacher spread0.297 · 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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Citations1
Published2013
Admission routes1
Has abstractyes

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