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Record W2346314070 · doi:10.1177/2325967116s00108

Characterization of Posterior Glenoid Bone Loss

2016· article· en· W2346314070 on OpenAlexaff
Adam B. Yanke, Rachel M. Frank, Jason J. Shin, Geoffrey S. Van Thiel, Nikhil N. Verma, Brian J. Cole, Anthony A. Romeo, Matthew T. Provencher

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicShoulder and Clavicle Injuries
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicinePosterior shoulderShouldersIliac crestNuclear medicineTibiaOrthodonticsSurgery

Abstract

fetched live from OpenAlex

Objectives: The purpose of this study was to characterize the morphology and location of posterior glenoid bone loss in pat ients with posterior instability instability utilizing computed tomography (CT). Methods: Clinical data was selected for patients with posterior shoulder instability that had undergone posterior stabilization (open or arthroscopic) or posterior osseous augmentation (distal tibia or iliac crest). Three fellowship-trained surgeons from two institutions contributed patients. Pre-operative CT data was collected for all patients. The axial cuts were segmented and reformatted in three-dimensions for glenoid analysis using Osirix. From this three-dimensional model, the following was calculated: percent bone loss (Nobuhara), total arc of the defect (degrees), and a clock-face description (start point, stop point, and average or direction). Pearson correlation coefficients were performed using significance of p<0.05. Results: Fifty shoulders from 50 patients were reviewed. Fourteen patients (average age 30 years; 93% male) had evidence of posterior glenoid bone loss and were included for evaluation. Defects on average involved 13.7±8.6% of the glenoid (range, 2-35%). The average start time (assuming all right shoulders) on the clock face was 10 o’clock ± 40 minutes and stopped at 6:30 ± 25 minutes. The average direction of the defect pointed toward 8:15 ± 25 minutes. The percent bone loss correlated with the total arc of the defect (Pearson: 0.93, p<0.05, R2: 0.86) and the direction of the bone loss (Pearson: 0.64, p<0.05, R2: 0.40). The direction of bone loss significantly moved more posterosuperior the larger the defect became (Pearson: 0.63, p<0.05, R2: 0.40). Conclusion: Posterior bone loss associated with posterior glenohumeral instability is typically directed posteriorly at 8:15 on the clock. As defect get bigger, this direction moves more posterosuperior. This information will help guide clinicians in understanding the typical location of posterior bone loss aiding in diagnosis, cadaveric models, and treatment.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.297
Teacher spread0.287 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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