A Three-Dimensional Computed Tomography Radiographic Study -- Can We Predict Glenoid Width Based on Glenoid Height? (216)
Bibliographic record
Abstract
Objectives: The purpose of this study was to investigate the relationship between glenoid width and other morphologic parameters using three-dimensional (3D) computed tomography (CT) images of native shoulders in hopes of generating a formula to predict glenoid width which will have utility in planning boney shoulder stabilization surgeries. Methods: 102 glenoid images were obtained for patients who underwent contralateral shoulder glenoid reconstruction for anterior shoulder instability between 2012 and 2020. Demographic data was obtained including age, gender and BMI. The subjects were excluded if they had a prior history of ipsilateral shoulder instability, shoulder fractures, or bone tumors. The following glenoid parameters were measured: width (W), height (H), ratio (W/H), anteroposterior (AP) depth, superior-inferior (SI) depth and version. The shape of the glenoid was also classified into pear, inverted comma or oval. Data was analyzed based on gender and age. Simple logistic regression, Kruskal Wallis Rank tests and Fisher Exact tests were performed. Results: There were 71 male and 25 females with a mean age of 39.74 ± 17.88 years. Pear morphotype accounted for most glenoid shapes (46%). The glenoid width was strongly correlated with the height (coefficient = 0.78) and a regression model equation was obtained: W (mm) = 3.4 + 0.68*H (mm). There was also strong correlation with gender (P<0.0001), age (P=0.0384), BMI (P<0.0001), glenoid shape (P=0.0036), height (P=0.0019), AP and SI depths (P<0.0001). Male gender was associated with higher measurement values for all parameters. Older age was significantly correlated with higher glenoid width values in both male and females group. (P=0.0015 and P=0.0104, respectively). Conclusions: The native glenoid width can be easily estimated using solely the glenoid height. This is particularly important for surgical decision making when facing anterior or posterior glenoid defects in patients with shoulder instability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".