Calculating glenoid bone loss based on glenoid height using ipsilateral three‐dimensional computed tomography
Bibliographic record
Abstract
PURPOSE: To investigate the relationship between glenoid width and other morphologic parameters using three-dimensional (3D) computed tomography (CT) images of native shoulders, and to create a new measurement tool to assess glenoid defects in a Canadian population with established anterior shoulder instability. METHODS: Forty-three glenoid CT scans were analyzed for patients who underwent contralateral shoulder glenoid reconstruction for anterior shoulder instability between 2012 and 2020. Demographic data were 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), anteroposterior (AP) depth, superior-inferior (SI) depth and version. The shape of the glenoid was also classified into pear, inverted comma or oval. RESULTS: = 0.9) and a regression model equation was obtained: W (mm) = 2.5 + 0.7*H (mm). There was also strong correlation with gender (P < 0.001), glenoid shape (P = 0.030), AP and SI depths (P = 0.006 and P < 0.001, respectively). Male gender was associated with higher measurement values for all parameters. The most common glenoid shapes were the pear (46.5%) and oval morphotypes (39.6%) for the whole study group. CONCLUSION: The native glenoid width can be estimated based on glenoid height using ipsilateral 3D CT. This may help with preoperative planning and surgical decision-making for patients with anterior shoulder instability and glenoid bone loss. LEVEL OF EVIDENCE: III.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".