Templating in shoulder arthroplasty – A comparison of 2D CT to 3D CT planning software: A systematic review
Bibliographic record
Abstract
BACKGROUND: Computed tomography (CT) utilizing computer software technology to generate three-dimensional (3D) rendering of the glenoid has become the preferred method for preoperative planning. It remains largely unknown what benefits this software may have to the intraoperative placement of the components and patient outcomes. PURPOSE: The purpose of this systematic review is to compare 2D CT to 3D CT planning in total shoulder arthroplasty. STUDY DESIGN: Systematic review. METHODS: A systematic database search was conducted for relevant studies evaluating the role of 3D CT planning in total shoulder arthroplasty. The primary outcome was component placement variability, and the secondary outcomes were intra- and inter-observer reliability in the context of preoperative planning. RESULTS: Following title-abstract and full-text screening, six eligible studies were included in the review (n = 237). The variability in glenoid measurements between 3D CT and 2D CT planning ranged from no significant difference to a 5° difference in version and 1.7° difference in inclination (p<0.05). Posterior bone loss was underestimated in 52% of the 2D measured patients relative to 3D CT groups. Irrespective of 2D and 3D planning (39% and 43% of cases respectively), surgeons elected to implant larger components than those templated. There was no literature identified comparing differences in time, cost, functional outcomes, complications, or patient satisfaction. CONCLUSION: The paucity of evidence exploring clinical parameters makes it difficult to comment on clinical outcomes using different methods of templating. More studies are required to identify how improved radiographic outcomes translate into improvements that are clinically meaningful to patients.
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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".